# Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered
Architecture and Future Roadmap

Saeed Ahmadi1

1School of Computer Science,University of Guelph, Ontario, Canada
<EMAIL_ADDRESS>Maliheh Salehfar2
2School of Management and Accounting
Allameh Tabataba’i University, Tehran, Iran
<EMAIL_ADDRESS>
###### Abstract

Privacy-Preserving Cloud Computing is an emerging technology with many
applications in various fields. Cloud computing is important because it allows
for scalability, adaptability, and improved security. Likewise, privacy in
cloud computing is important because it ensures that the integrity of data
stored on the cloud maintains intact. This survey paper on privacy-preserving
cloud computing can help pave the way for future research in related areas.
This paper helps to identify existing trends by establishing a layered
architecture along with a life cycle and an ecosystem for privacy-preserving
cloud systems in addition to identifying the existing trends in research on
this area.

###### Index Terms:

Cloud Computing, Privacy, Secure Cloud, Privacy-Preserving Cloud, Trend
Analysis, Future Roadmap.

##  1 Introduction

In recent years, cloud computing has been of great interest to the research
community [1][2]. It is used in a variety of applications ranging from
healthcare [3][4] to geosicences [5] and autonomous vehicles [6, 7]. Cloud
computing plays a significant role in modern technology as it is actively
interacting with other technological fields such as edge [8] and fog computing
[9], Internet of Things (IoT) [10], sensor technology [11] and big data
[12][13]. ”Edge Computing has been defined as a distributed computing topology
in which the storage and computing has been perfume close to the source of
data.Fog Computing has been defined as decentralized computing infrastructure
that perfume between the data source and the cloud. Enabling technologies that
support cloud computing vary from real-time computing [14] and virtualization
[15] to artificial intelligence [16][17][18]. Several design objectives
including performance [19], reliability [20] and fault tolerance [21] are
considered in the design of cloud computing systems. Especially, concerned
about different aspects of security including attack resilience [22, 23][24]
and confidentiality [25][26] have been of concern to cloud computing
researchers. However, privacy seems to be the most critical security aspect in
cloud computing [27][28][29].

In this paper, we first establish an ecosystem for privacy-preserving cloud
including applications, vehicular technology, healthcare, social networking,
Internet of Things (IoT), smart home, big data, and data centers. Then we
developed a life cycle and reviewed it in these stages: design, verification,
implementation, and deployment. In the next step, we build a layered
architecture containing Privacy-Preserving Infrastructure, Privacy-Preserving
Services, and Privacy-Preserving Computing. Lastly, we establish a future
roadmap in Quantum-Inspired and Bio-Inspired AI. We present our paper with the
hope that we can motivate others to study these topics further as these new
technologies develop. It should be noted that although there may be existing
survey papers related to the topic at hand, they have shortcomings which
motivate our work in this survey. It is our intention to cover the topic of
Privacy-Preserving Cloud Computing as comprehensively as possible.

The rest of this paper is organized as follows. Section 2 summarizes existing
surveys. Section 3 outlines the applications and technologies that comprise
the ecosystem for cloud computing. Section 4 outlines the full 4-stage life
cycle of a cloud system . Section 5 describes the layers which make up the
architecture of a privacy-preserving cloud system. Finally, Section 6 outlines
a future roadmap for cloud computing and how Artificial Intelligence (AI) can
be used to create new technologies.

Privacy is important in cloud computing because it ensures that data shared in
the cloud can only be accessed by those who have been given permission.
Without proper data privacy, the integrity of data stored in the cloud would
not be able to be guaranteed. In Figure 1, Artificial Intelligence and
Cryptography combine together, resulting in a Privacy-Preserving Cloud system
where privacy is built into the foundation using AI and Cryptography concepts.

Figure 1: Privacy Incorporated in the Design of Cloud Computing Environments

In Figure 1, the blue cloud represents Artificial Intelligence and the Lock
and Key represent Cryptography. The arrows leading from these symbols point
towards a plus symbol, signifying their union. The result of this combination
is Privacy-Preserving Cloud, represented by the joining of the two symbols.

##  2 Existing surveys

There are many surveys on the security of cloud computing. However, some of
them are too outdated for such a dynamic research area. Others do not focus on
the privacy of cloud computing [30]. Some existing surveys study cloud
computing only in specific applications, and some of them fail to develop a
future roadmap. These shortcomings motivate our work in this paper.

####  2.0.1 Surveys on Challenges, Issues, Concerns and Requirement of Cloud
Computing

Storing data deals with many challenges in security and privacy, and Cloud
Service Providers should take appropriate steps to overcome these challenges
[31]. In this section, previous studies and outputs have been reviewed.
Although Mobile Cloud Computing (MCC) provides a way to meet our needs for
abundant resources in transportable devices, there is a lack of security and
confidentiality. To that end, in [32] security issues and challenges in mobile
cloud computing have been analyzed. In [33] Data Integrity (DI), Data
Confidentiality (DC), and availability that are very important in cloud
storage have been studied. Furthermore, one of the famous and popular
technology for organizations and personal users that Cloud Computing has
provided is Storage as a Service(StaaS). The security and privacy challenges
according to growing concern in STaaS have been presented in [34]. Decreasing
security risks in Cloud Computing is the fundamental action that has been
analyzed in [35], [36] that authors proposed solutions to overcome. These
concerns were further studied in [37] and [38] since that is not obvious where
the data is stored and controlled. In [39], the Pixel key pattern and Image
Steganography techniques have been proposed to solve insuperable data security
problems in Cloud Computing but in [40] potential issues for gathering access
to users data and harming their privacy have been studied in order to attract
urgent attention. Three threats are data breaches, hijacking of accounts, and
multitenancy that brought about security issues that have been analyzed in
[41], and predicted that if these issues have been solved, cloud computing
will be more widespread. Although The requirements and solutions of cloud
computing security were reviewed comprehensively in [42], its publication year
is old, and its solution may not be helpful for present technology according
to fast development. In [43] and [44], similar algorithms have been proposed
to overcome the obstacles in Cloud Computing under three fundamental
categories, cryptographic, data storage, and data semantics. Moreover,
Information that users should be aware of to estimate risks related to
preserving their data was presented in [45], but the privacy of users was not
under observed.

###  2.1 Surveys on the Evolution and Future Foresight

Cloud Computing and Blockchain technology development have been examined in
[46, 47], and how these technologies can integrate and improve each other
drawbacks were under observation.

###  2.2 Surveys on the Applications of Cloud

####  2.2.1 Applications in Healthcare and Medical Systems

In [48], the authors argued the security peril of cloud service platforms in
the field of medicine and also provided related solutions. Protecting privacy
and security of users and data were presented in [48] and explained a method
that can refuse illegal users permission.

###  2.3 Surveys on Secure Cloud

The existing surveys related to infrastructure and the concept of security in
Cloud Computing have been reviewed in this section. Likewise, in the first
subsection of this section, we investigate the general security concept in
Cloud Computing, and in the subsequent subsections, privacy and security of
data, storage, communication [49] have been studied. Moreover, cryptographic
and steganography methods, and Secure Data Sharing risks have been reviewed in
current surveys subsequently. Following sections, authentication and security
auditing, as well as data provenance and challenges and risks, threats and
attacks, have been investigated.

####  2.3.1 The General Concept of Security

In [50] authors came up with a method for solving the problem related to
enterprise users, cloud operators, and regulators by analyzing current
security and privacy challenges, in addition in [51], authors have analyzed
the solution of different categories of risks in cloud systems such as
infrastructure threats, host threats, and service providers threats similar to
[52] that authors have introduced a solution to overcome security issues
related to confidentiality, integrity, availability, authentication. In
comparison to [53], where the authors have viewed the privacy and security of
data from the perspective of appropriate training because the consultant is a
crucial part of service providers that are dealing with the implementation.
The authors in [54] have observed the effect of extended cloud such as Mobile
Edge Computing (MEC) and fog on network schematic of cloud computing; also,
they analyzed models and architectures of technologies due to the security and
flexibility that is required while in [55], the authors have analyzed the
algorithms that come from biological In order to deal with security issues of
cloud computing. In addition, the authors in [56] have come up with an
innovative method which is Cloud Bursting Brokerage and Aggregation (CBBA),
and analyzed the secure sharing mechanism to reduce cloud-bursting with
denial-of-service (DoS) attacks or distributed denial-of-service (DDoS)
attacks that have deleterious effects on cloud computing.

As opposed to [57] that Secure Smart Grids in cloud-based software platforms
was observed, the authors in [58] have explained the model, control, and
management of the rechargeable batteries such as Li-ion batteries used in IoT.
Furthermore, the vulnerabilities and threats in the live migration of Virtual
Machine have been presented in [59] and claimed that there is an inadequate
solution to provide security. Moreover, Sun and Aida have indicated how they
meet the security of user applications with a method for running applications
that need huge resources on both local computing resources and Infrastructure-
as-a-Service Cloud (IaaS) by making use of machine learning in [60] similar to
[61] where the authors have demonstrated how they came over several basic
issues and improved CPU frequency in Virtual Machine with Workflow Scheduling.
In addition, the security of live migration in virtualization has been
analyzed in [62], and authors have introduced three techniques to cover
confidentiality and integrity of data, authentication, and authorization of
operations.

####  2.3.2 Privacy-Preserving Cloud

A framework of privacy protection in cloud computing has been demonstrated in
[63], and the authors have analyzed the concept of encryption and security and
draw future directions, while the authors in [64] reviewed a number of
articles related to the privacy and security of electronic health records in
cloud architecture and the cryptographic and noncryptographic of electronic
health records protection. In addition, the vulnerabilities and threats in
Cloud Computing have been analyzed in terms of confidentiality, integrity,
availability, accountability, and privacy-preservability in [65] and they have
proposed strategies for protection. Also, for dealing with challenges and
improving the level of privacy in Cloud Computing, Privacy Enhancing
Technologies was studied by authors in [66] in order to identify a method. The
authors have demonstrated a rundown of technologies and patterns in order to
protect and encrypt data in [67] and emphasized issues and tackles that can be
studied in the future in Vehicular Cloud Computing in [68] by reviewing and
examing the structure and concept of cloud storage and adventures and risks of
security and privacy of cloud storage. Although, the authors in [69] argued
the most advanced method of privacy-preserving that is utilized in e-Health
clouds as well as explained categorization of cryptographic and
noncryptographic approaches, in [70] the security and privacy of health data
stored in the cloud have been reviewed in order to indicate the advantages and
disadvantages of eHealth’s security and privacy algorithms. Several
implementation techniques that can help developers and a number of privacy
issues that should be attended to in the could systems environment have been
presented by authors in [71]. As opposed to [72] where the number of analyses
that carried out in cloud computing has been examined in order to make know
threats related to cloud computing architecture, authors in [73] argued
security and privacy issues in order to provide appropriate solutions.
Moreover, an encryption data method which is the Bloom filter technique has
been presented in [74].

####  2.3.3 Secure Data

Data Security is vital and important in cloud computing [75]. The encryption
algorithms have been analyzed in terms of providing the security of data in
storing and transferring in [76] similar to [77] in order to overcome the
challenges in the security of information. In [78], the perceptions of audit
and data security that is the fundamental obstacle that should pay attention
to having been analyzed, while in [79] Multi-Cloud Database (MCDB) has been
reviewed, and they explained its drawback, which is the multi-stage cloud that
should be considered in order to make it simple in the future. Furthermore,
cloud services such as Software as a Service (SaaS), Platform as a Service
(PaaS), Infrastructure as a Service (IaaS) have been analyzed in terms of
cloud computing security issues in [80], and also the authors provided some
solutions for security and overcome its drawbacks. In [81], the Mobile Cloud
Computing data security frameworks have been under observation to recognize
obstacles, issues, and security measures for improvement in future
implementation. Moreover, encryption and decryption algorithms have been
presented in [82] and authors have proposed new algorithms that improve the
security and efficiency of data in cloud computing.

##### Data Security Standards

The existing method of integrity, authentication, and confidentiality of data
in cloud computing has been presented in [83] and [84]; also the authors
analyzed security techniques, issues, and challenges to improve data security
measures in cloud computing.

####  2.3.4 Secure Storage

There is growing concern about storing data in the cloud [85]. In [86] the
authors have demonstrated how the secret key, used in encryption and
decryption, has affected the efficiency and security of bandwidth and storage
space. The security of data storage related to diabetes disease in cloud
computing was under observation in [87], and authors brought up some issues
that need to be considered for the framework that collects information similar
to [88] where several cloud data storage infrastructure and security
algorithms were presented and offered the accountability process, and
protection measures that need to be developed in further study. In addition,
the effects of data security in cloud storage on integrity, confidentiality,
and availability were analyzed in order to recognize security requirements
that have been analyzed in [89]. Also, encryption and decryption algorithms
used in cloud storage were analyzed and compared with related techniques to
improve their performances in [90]. Authors have claimed sharing operation and
searching operation architect needs to develop and design securely in the
future. In contrast, in [91] challenges and limitations were observed in terms
of data security in these systems and authors have argued how security and
usage of information provided in various search systems in cloud storage
similar to [92] that authors have analyzed security algorithms and techniques
used in the client-side and vendor-side. Moreover, The advantages of using
blockchain in cloud computing were presented in [93] as well as authors
analyzed how these methods improve the security of data in cloud computing due
to its decentralized nature. Likewise, The authors in [94] have analyzed
security data and provided solutions to improve third parties infrastructure
in terms of safety, privacy, and availability measures.

####  2.3.5 Secure Communication

For reviewing secure communication subjects in current surveys, Soni and Kumar
have analyzed methods and algorithms related to cloud computing security to
find the gap between future development and previous research, and they gave
prominence to future suggestions.

####  2.3.6 Cryptographic Security

Cryptographic Security is one of the fundamental methods for providing
security and privacy of data in Cloud Computing [95]. Proxy re-encryption,
which can encrypt data in cloud computing with user private key, has been
under observation in [96] and authors, by comparing it with current algorithms
and measures have introduced better security and efficient data solutions
similar to [97] where the security schema and design of public-key encryption
with equality test  (PKE-SF) was discussed and authors have examined its
feature efficiency and proposed some helpful directions. In addition, various
cryptographic techniques in the public cloud have been reviewed in [98], and
demonstrated how these techniques could preserve data from malicious activity
in cloud computing and highlighted their issues similar to [99] that several
security issues were presented, and authors proposed various solutions for the
purpose of getting to know about them for increasing security in cloud
conditions. Furthermore, in [100] and [101] the authors compared several
cryptographic methods and demonstrated differences in cost and performance,
and genetic algorithms and visual cryptography have been proposed for
concealing high-priority data similar to [102] that authors have proposed
cryptographic techniques that improve the security of cloud storage.

####  2.3.7 Steganography

In [103] authors have described its related techniques: the Least Significant
Bit (LSB) and the Discrete Cosine Transform (DCT).

####  2.3.8 Secure Data Sharing

The security of data sharing is one of the most critical steps in Cloud
Computing [104]. Authors in [105] have introduced several methods that provide
data sharing security in the cloud environment but mainly analyzed Ciphertext
Policy Attribute-Based Encryption(CP-ABE) that has beenused in integrity
policies.

####  2.3.9 Risks, Threats and Attacks

In [106], the authors reviewed several papers and identified several security
issues that affected the cloud computing environment. They conclude that the
blockchain mitigates security concerns for service providers and users similar
to [107] that authors have proposed Advanced Security Measures and Practices
to improve the structure of current technology to deal with security risks and
challenges. The structure of the blockchain method has been analyzed in [108],
and the authors proposed a secure method of blockchain that can overcome
security issues and risks as opposed to [109] where the authors have explained
the vulnerabilities, attacks, and threats related to cloud services and
infrastructures. In addition, the benefits and drawbacks of current security
methods have been analyzed in [110], and the authors argued potential
capabilities in the security of cloud service in order to overcome
disadvantages in comparison to [111] that authors did a thorough investigation
in the security of cloud infrastructure and offered a method that can improve
issues and risks in that time. However, due to the year when the paper was
conducted, this method would not be effective. In addition, In [112], authors
conducted a detailed analysis on papers between 2019 and 2020 to help
companies be aware of cloud computing vulnerabilities and prioritize due to
the widespread use of cloud services as opposed to in [113] that risk
management methods that are different from those in IT enterprise have been
analyzed in cloud systems and authors have demonstrated its efficiency in
cloud security challenges by observing technically. The accuracy of  Intrusion
Detection Systems (IDS)  have been analyzed in cloud base malicious, and
attacks in [114] and authors have reported IDS which utilizes multiple method
detection to improve its efficiency in the cloud while in [115] the current
security framework has been analyzed to accentuate the disadvantages and
drawbacks and authors have implemented new  Intrusion Detection Systems(IDS)
and Prevention System (IPS) that improve security in virtual networks.

####  2.3.10 Authentication

In [116], the authors have made use of Kerberos as a third-party auditor for
authentication, RSA algorithm (Rivest–Shamir–Adleman) for security, and MD5
algorithm to provide integrity and also how the cloud systems provide security
when information data is being transferred. In addition, in [117]; the
duplication process has been presented in detail, and new techniques in a
cloud architecture that encrypts duplicate-check tokens of files with private
keys have been presented.

####  2.3.11 Data Provenance

The analysis of current provenance management schemes in the cloud system and
their security solution was presented in [118].

####  2.3.12 Security Auditing

The advantages and drawbacks of several Third Party Auditing (TPA) have been
analyzed in [119], and the authors have concluded that nearly all TPA methods
need to be developed in terms of the security of public auditing.

###  2.4 Surveys on Cloud-Based Big Data

Big Data refers to an enormous amount of data that has been produced these
days by different sources, Such as IoT devices. Since storing a vast amount of
data in Cloud Computing is becoming a challenge for service providers.
Different security methods of eHealth data in the cloud have been presented in
[120], also in order to improve security, and the authors came up with a decoy
technique that is storing the patient data initially in the cloud and another
in the fog layer. In contrast, in [121], prior research in the security of
storage management has been analyzed, and authors have proposed a new
technique.

###  2.5 IoT-Based Cloud and Cloud-Based IoT

Internet of Things (IoT) devices is widespread these days since there is no
doubt in the benefits of those devices, but when it comes to private data, the
security mechanism has played a vital role [122]. This section studied IoT and
cloud computing, two concepts that have become so interwoven and almost
inseparable. Several infrastructures of IoT were analyzed effectively from the
perspective of security in [123], and authors have observed them by conducting
the man-in-the-middle attack between end devices sensor and service providers
while in [124] just the security of data in the Internet of Things (IoT)
integrated with Cloud base internet has been analyzed, and risks and threats
in the Internet of Things have been highlighted. Moreover, the security risks
related to storing and collecting data from IoT devices that need urgent
action have been presented in [125]. In addition, the authors came up with
solutions that may be helpful as opposed to [126] that threats, risks,
vulnerabilities of  Supervisory Control And Data Acquisition (SCADA) systems
have been analyzed, and some crucial issues reported. WebSCADA is an
application utilized for innovative medical technologies.

###  2.6 Surveys on AI-Assisted Secure Cloud

The authors reviewed 60 articles in order to demonstrate challenges and
accuracy estimation of using machine learning and the type of machine learning
in cloud security [127]. The efficiency of facial recognition techniques in a
cloud environment from data security has been analyzed in [128]. Cloud data
behavior detection and recognition are the major components that will
contribute to developing a secure data surveillance system implemented in a
cloud environment that will protect stored data from network intrusions and
ensure data security.

In this location, you need a table. Each row of the table is allocated to one
of the above surveys. The first coulmn in each row cites the related survey
paper. The second row contains the publication year of the survey. The third
row indicates whether or not the survey focuses on the privacy of cloud
computing (Yes/No). The fourth column Indicates whether or not the survey
presents a future roadmap (Yes/No). The fifth column indicates whether or not
the survey studies cloud computing in a specific application (Yes/No).

The summary of previous studies related to Privacy-Preserving in Cloud
Computing has been presented in Table I and Table II. Table I includes from
[32] to [90] and Table II from [91] to [128]. The second column refers to the
published year, and the third column shows whether the paper focuses on the
privacy of cloud computing or not. The next column indicates whether the paper
creates a future road map or not, and the last column specifies if they have
been studied in a specific application. In both tables, the papers have been
reviewed as 2010. In the Table I, just 13 papers focus on privacy in cloud
computing, while in Table II, no paper focuses on privacy in cloud computing.
Some papers have drawn a future road map and analyzed cloud computing in a
specific application.

Table I: Summary of previous studies in Privacy-Preserving Cloud Computing - Part 1 Published |  Year |  NN-focuses on the privacy of cloud computing |  a future roadmap |  cloud computing in a specific application
---|---|---|---|---
[32] |  2018 |  no |  no |  no
[33] |  2019 |  no |  no |  no
[34] |  2019 |  no |  no |  yes
[35] |  2020 |  yes |  no |  no
[36] |  2015 |  no |  yes |  no
[37] |  2015 |  no |  yes |  no
[38] |  2015 |  yes |  yes |  no
[39] |  2015 |  yes |  yes |  yes
[40] |  2014 |  yes |  no |  no
[41] |  2016 |  no |  no |  no
[42] |  2012 |  no |  yes |  no
[43] |  2017 |  no |  yes |  yes
[44] |  2020 |  no |  yes |  no
[45] |  2015 |  no |  no |  no
[129] |  2018 |  no |  no |  yes
[48] |  2013 |  no |  no |  yes
[50] |  2013 |  no |  no |  no
[51] |  2017 |  no |  no |  no
[52] |  2016 |  no |  no |  no
[53] |  2017 |  no |  no |  yes
[54] |  2020 |  no |  yes |  yes
[55] |  2011 |  no |  no |  yes
[56] |  2014 |  no |  no |  no
[57] |  2017 |  no |  no |  yes
[58] |  2013 |  no |  no |  no
[59] |  2013 |  no |  no |  yes
[60] |  2010 |  no |  no |  yes
[61] |  2018 |  no |  no |  yes
[62] |  2014 |  no |  no |  yes
[63] |  2019 |  yes |  yes |  no
[64] |  2019 |  yes |  no |  yes
[65] |  2012 |  yes |  no |  no
[66] |  2021 |  yes |  yes |  no
[67] |  2020 |  yes |  no |  no
[68] |  2020 |  yes |  yes |  yes
[69] |  2014 |  yes |  yes |  yes
[70] |  2021 |  yes |  yes |  yes
[71] |  2014 |  yes |  no |  no
[72] |  2017 |  no |  no |  no
[73] |  2016 |  no |  no |  no
[74] |  2018 |  no |  no |  no
[76] |  2019 |  no |  no |  no
[77] |  2021 |  no |  no |  no
[78] |  2018 |  no |  no |  no
[79] |  2020 |  no |  yes |  yes
[80] |  2021 |  no |  no |  no
[81] |  2019 |  no |  yes |  yes
[82] |  2020 |  no |  no |  no
[83] |  2020 |  no |  no |  no
[84] |  2018 |  no |  no |  no
[87] |  2017 |  no |  no |  yes
[88] |  2020 |  no |  yes |  no
[86] |  2015 |  no |  no |  yes
[89] |  2019 |  no |  no |  no
[90] |  2021 |  no |  yes |  no
Table II: Summary of previous studies in Privacy-Preserving Cloud Computing - Part 2 Published |  Year |  NN-focuses on the privacy of cloud computing |  a future roadmap |  cloud computing in a specific application
---|---|---|---|---
[91] |  2021 |  no |  no |  no
[92] |  2020 |  no |  no |  no
[93] |  2020 |  no |  no |  yes
[94] |  2019 |  no |  no |  no
[130] |  2017 |  no |  yes |  no
[96] |  2016 |  no |  yes |  yes
[97] |  2021 |  no |  no |  yes
[98] |  2016 |  no |  no |  no
[99] |  2021 |  no |  no |  no
[100] |  2014 |  no |  no |  no
[101] |  2017 |  no |  no |  no
[102] |  2014 |  no |  no |  yes
[103] |  2019 |  no |  no |  yes
[105] |  2021 |  no |  no |  yes
[106] |  2021 |  no |  yes |  no
[107] |  2020 |  no |  no |  no
[108] |  2020 |  no |  no |  no
[109] |  2020 |  no |  no |  no
[110] |  2015 |  no |  no |  no
[111] |  2015 |  no |  no |  no
[112] |  2021 |  no |  no |  no
[113] |  2015 |  no |  no |  no
[114] |  2015 |  no |  no |  no
[115] |  2015 |  no |  no |  no
[116] |  2015 |  no |  no |  no
[117] |  2017 |  no |  no |  no
[118] |  2018 |  no |  no |  no
[119] |  2019 |  no |  yes |  no
[120] |  2019 |  no |  no |  yes
[121] |  2018 |  no |  no |  no
[123] |  2021 |  no |  no |  yes
[124] |  2021 |  no |  no |  yes
[125] |  2020 |  no |  no |  yes
[126] |  2016 |  no |  no |  yes
[131] |  2021 |  no |  no |  yes
[128] |  2020 |  no |  no |  yes

##  3 Ecosystem

Privacy-Preserving Cloud can have many uses in different sectors in order to
create an ecosystem [132]. In Section 3.1, we explore the various applications
of this technology and examine how it can be beneficial in these areas. The
technology which allows for these applications to function is explored in
Section 3.2. In order to keep these ecosystems secure, there are various
technologies and mechanisms which can be used. Section 3.3 covers several of
these security measures. Finally, in Section 3.4, this paper will take a look
at the security challenges which come along with using a cloud-based
ecosystem.

Figure 2: Applications, Enabling Security Mechanisms, and Enabling Sciences
and Technologies joining to form an Ecosystem for Privacy-Preserving Cloud.

In Figure 2, a base for the ecosystem is formed by Enabling Sciences and
Technologies such as Optimization [133], Blockchain [134], PUF, Edge
Computing, Fog Computing, and Wireless Sensor Networks [135]. On top of this
base stands the pillars consisting of the Enabling Security Mechanism such as
Anonymization, Privacy-Preserving Authentication, Privacy-Preserving Access
Control, Privacy-Preserving Cryptography, and Privacy-Preserving Watermarking.
Finally, Applications rest on top of the structure because they are enabled by
the technologies below them. These applications include Data Centers, IoT,
Smart Homes, Big Data, Industrial Environments, Vehicular Technology,
Healthcare, and Social Networking.

###  3.1 Applications

####  3.1.1 Industrial Environments and IIoT

The Industrial Internet of Things is a subset of the Internet of Things which
works to optimize industrial production by providing tools with additional
connectivity and intelligence, as detailed in [136, 137]. In the healthcare
industry, the Internet of Things works to improve services for patients with
devices that can more effectively monitor them and collect their health data.
[138] describes how devices implanted in or worn by a patient can allow for a
doctor to remotely diagnose a patient. [139] outlines a method of protecting
the privacy of industrial data is to encrypt it before sending it to the
cloud, have operations performed on the encrypted data, and returning
encrypted results to be decrypted by the user.

####  3.1.2 Vehicular Technology

The ourline of [140] indicates how cloud technology integrated into vehicles
allows for road traffic to be improved with devices such as sensors, cameras,
and wireless transceivers. The authors of [141] say that it also helps create
a network for autonomous vehicles so that data can be stored and processed
remotely.In [142], the authors proposed the use of this technology in
vehicular advertisement dissemination which allows for vehicles to receive
advertisements from a cloud network . Vehicular cloud technology allows for
more complex computations, such as calculating traffic and determining optimal
routes, to be offloaded to parked cars which do not have to perform
computations of higher priority, as said by [143].

####  3.1.3 Healthcare

In [144], authors state that cloud-based data-driven healthcare monitoring
services have allowed for a rapid growth of healthcare data which assist with
eHealthcare. To protect this medical data, some networks will use a blind
cloud framework which replaces patient identities with generated pseudo-
identities. The authors of [145] describe how this allows for unidentified
data to be stored and analyzed. Searchable encryption schemes can also be used
so that health data can be searched for while maintaining privacy as outlined
by [146]. Searchable symmetric encryption (SSE) is a method that provides the
ability for users to utilize the storage of another party (a server) for
privately hosting data that is encrypted. Therefore, the cloud service
provider (CSP) does not have access to them, and when a user wants to access
the data, CSP provides the encrypted data for them.

####  3.1.4 Social Networking

In [147], it is described how many social networking platforms store user
information on cloud services, so it is very important to keep this data
secure. Techniques such as homomorphic encryption, oblivious transfer, and
secret sharing can be used to protect user data. ”Homomorphic encryption is a
method with which users can analyze or manipulate their encrypted data without
decryption.

####  3.1.5 IoT

Cloud technology has improved the Internet of Things’ ability to manage large
amounts of data. Flexible privacy-preserving data sharing allows a user to
send encrypted data using an identity-based encryption scheme that is outlined
in [148]. In addition, a network attestation scheme outlined by [149] can be
used to ensure that scalability, forward-security, and privacy.

####  3.1.6 Smart Home

Privacy-preserving smart homes have the ability to connect a single house to a
cloud platform which aggregates the data from all other houses on the network.
The ourline of [150] indicates that this data can be used to increase home
safety and energy efficiency in order to increase a residents quality of life.

####  3.1.7 Big Data

Because of mobile networks, data is constantly being generated at a rapid
pace.  In [151], authors state that this data generation is called big data
stream. The authors of [152] outline how how cloud computing can offer a
scalable infrastructure so that this big data stream can be processed and
aggregated.

####  3.1.8 Data Centers

As detailed by the authors of[153], Geo-distributed cloud data centers hold
and transfer large amounts of data every day. Due to privacy laws differing
based on region, it can make the transfer of data across borders difficult to
do. Data centers must ensure that they follow the strict data regulations put
in place by countries and organizations, such as  General Data Protection
Regulation (GDPR).

###  3.2 Enabling Sciences and Technologies

####  3.2.1 Optimization

In [154] , the authors describe how outsourcing computation-intensive tasks to
the cloud allows for systems to be more optimized, but comes at the risk of
more privacy and security issues.

####  3.2.2 Blockchain

In order to protect data stored on the Cloud, blockchain-based technology
[155] such as AuthPrivacyChain can be used to prevent hackers from accessing
resources as outlined in the framework devised by [156, 157]. In the [158]
paper, it describes how blockchain has also been used to implement forward and
backward privacy by enforcing searchable public-key encryption, and [159]
describes how it can act as a distributed public ledger so that health data
can be shared on a cloud-based system.

####  3.2.3 PUF

In [160], it is said that physical unclonable functions, or PUFs, provide the
ability to have lightweight physical identities for smart devices.Physical
Unclonable Functions (PUFs) introduce an extra player of physical security
without bringing any additional overhead to smart devices.

####  3.2.4 Mobile Computing

Mobile cloud computing allows for users to access cloud services through
wireless networks [161]. However, [162] outlines how wireless networks are
vulnerable to numerous threats which means more security measures must be
taken. Some such measures which can be taken are listed by [163], such as key
management for unlinkability, a secure indexing method, and integration of
attribute based encryption. In addition, the authors of [164] outline how
scalable and privacy preserving friend matching offers a scalable solution for
protecting social media user data over mobile networks. Authentication methods
such as privacy-aware authentication and MediBchain-based privacy-preserving
mutual authentication can also help protect data over mobile cloud networks,
as described by the authors of [165] and [166].

####  3.2.5 Edge Computing

In [167], it is described how mobile edge clouds deploy at the network edge in
order to allow mobile users to access cloud services. However, mobile social
sensing means that users need to report personal information which could
result in data theft. In [168], the authors propose that participant grouping
can work as one solution by leveraging secure sharing within groups.

####  3.2.6 Fog Computing

The authors of [169] claim that fog computing can be used to extend cloud
computations to the edge of a network and provide storage, geo-distribution,
and mobile support. Fog computing can be used to keep data on the cloud safe
by using a one-round authenticated key argument protocol as outlined by [170],
or by using a homomorphic Paillier cryptosystem as outlined by [171].
Homomorphic encryption is a method of encryption in which users can do
mathematical operations on encrypted data without decryption. The Paillier
cryptosystem, that Pascal Paillier has proposed in 1999, is a probabilistic
asymmetric algorithm for public key cryptography.

####  3.2.7 Wireless Sensor Networks

Wireless sensor networks are vulnerable to attack if the source node is
located.  ”Wireless sensor network refers to wireless sensors that communicate
and collect data, for instance, temperature, humidity, etc., from the
environment that have been installed. In [172], authors proposed that one
method of protecting the location of the source node is to change packet
destinations randomly in each transmission on the network. To compliment this,
a source-location privacy protection scheme based on anonymity cloud which can
also be used to protect to the location of the source node by constructing an
anonymity cloud around the source node  has been proposed in [173].

###  3.3 Enabling Security Mechanisms

####  3.3.1 Anonymization

In order to check that cloud infrastructure is meeting the intended security
standards, the practise of security auditing is often employed [174]. In order
to maintain privacy, input data and audit results are anonymized.”Data
Anonymization is the process that removes or alters the data that causes a
subject to be identified directly. a method called SegGuard uses per-tenant
encryption and property-preserving encryption so that the data is able to be
audited while still offering sufficient protection  has been proposed in
[175].

####  3.3.2 Privacy-Preserving Authentication

Many authentication schemes for tamper-proof devices have been proposed to
allow privacy and security in open-access environments. In [176] , the authors
explain how Vehicular Ad-Hoc Networks require such authentication so that they
may improve transportation networks. Because of the cloud, these vehicles need
to only register with a trusted authority once in order to authenticate as
outlined by the authors of [177]. In [178] the authors suggest that cloud
computing has allowed for data from biometric identification to be outsourced
to the cloud for computation, offering a more reliable way to authenticate a
user. The authors of [179] explain how this biometric data can be protected by
using a matrix-transformation-based privacy-preserving biometric
identification scheme using the property of the orthogonal matrix and
additional randomness. Additionally, the authors of [180] detail how
authentication can also be improved for mobile users by using the cloud to
support mutual authentication, key exchange, user anonymity, and user
untraceablilty. In [181], the authors suggest that a shared authority based
privacy-preserving authentication protocol can be used to address the issue
that a challenged privacy request on a shared network can reveal a user’s
privacy regardless of whether their request is granted. In a distributed
cloud-based system, an authorized accessible privacy model can be used to keep
data and identity private simultaneously  has been presented in [182].
Anonymous authentication can be achieved by using a registered security token
in conjunction with a key exchange which uses preauthentication and
postauthentication user anonymity to generate a pseudoidentity [183].

####  3.3.3 Privacy-Preserving Access Control

In order to facilitate resource sharing, the authors of [184] suggest that
cloud applications allow users to delegate their access permissions. Access
control policies guard the access to cloud resources and credentials. In the
bioinformatic field, large amounts of genomic data are stored and processed
over the cloud. The P2GT scheme detailed by [185] uses key-policy attribute-
based encryption to manage data access control. Mobile cloud computing can use
authentication schemes such as Self-Certified Public Key Cryptography (SCPKC),
Chinese Remainder Theorem (CRT), Ciphertext-Policy Attribute-Based Encryption
(CP-ABE), Scalable Media Access Control and Deduplication and (SMACD) to
manage access, as detailed by the authors of [186], [187], and [188]. In order
to enforce authorization for fine-grained access control on the cloud, the
authors in [189] state that an organization must use encryption.

####  3.3.4 Privacy-Preserving Cryptography

##### Data Encryption

According to [190], users can search encrypted documents on the cloud using
dynamic symmetric searchable encryption. Dynamic data encryption allows for
performance to be improved while working under time constraints [191] and
achieves a high level of privacy [192]. The authors in[193] suggest using
certificateless encryption keyword search to allow for multirecipient keyword
searching, which allows for privacy to be preserved on the Internet of Things.
Because the cloud is so open,  in [194] the authors claim that identity-based
encryption is important to maintain accountability. The authors of [195] state
that user identities can even be formed by combining a user’s identity with
the identity of their attribute authority. Using asymmetric scalar-product-
preserving encryption, in [196] authors indicate that fuzzy multi-keyword
search can be achieved.

##### Encrypted Data Processing

With the popularity of cloud computing, the authors of [197] claim that more
and more documents are encrypted before they are outsourced. Due to concerns
about the privacy of data on the cloud, the authors in [198] suggest a
geometric range query scheme can be used to protect the location of the data .
A keyword search is a common way of filtering encrypted data for use,
according to [199], and can be performed using  Multi-keyword Ranked Search
over Encrypted (MRSE) and Multi-keyword Ranked Search over Encrypted data in
Hybrid Clouds (MRSE-HC) schemes, as per [200] and [201]. To verify the results
of these multi-keyword searches, a scheme known as VPSearch  has been
introduced in [202] which can be used. Another way to efficiently access data
is to use a privacy-preserving outsourced similarity test in order to capture
the desired data which has been laid out by the authors of [203]. Frequent
itemset mining is a popular way to data mine on large cloud datasets [204]. To
analyze big graphs that have been outsourced to the cloud, spectral analysis
can be used as desecribed by [205]. When querying for graphs, constrained
shortest distance can help by finding the shortest distance from an origin to
a destination of a graph [206]. Cloud-based Personal Health Record systems are
used to manage health records securely by ensuring that submitted data is
encrypted [207]. Data retrieval confidentiality can be supported using
Searchable Encryption, as outlined by [208], to allow a trusted server to
access user data. [209] says that tensor decomposition can also be used so
that multiple users can have their data retrieved without the cloud learning
about the users data.

####  3.3.5 Privacy-Preserving Watermarking

In [210], authors define  Watermarking as when a watermark is embedded into
media so that it may be identified. Privacy-Preserving Watermarking allows for
a party to entrust the task of watermarking data to a cloud service.

####  3.3.6 Privacy-Preserving Integrity Checking and Auditing

While cloud storage offers easy maintenance and management of stored data, the
authors of [211] say that it does not ensure integrity. Remote data integrity
checking can be used to verify that data stored on the cloud is stored
honestly [212]. A third party auditor can be used to audit data to ensure
integrity, as can dynamic data integrity auditing as stated by the authors of
[213] and [214]. A Provable Data Possession proposed by [215] can allow people
to audit data efficiently by outsourcing data into small blocks of data and
then blinding them so that each block can be individually verified.

###  3.4 Related Security Challenges

####  3.4.1 Trust and Reputation

##### Trustable Computing

The authors of [216] state that in Cloud Computing, privacy and trust create
an inherent conflict. In order to evaluate the quantification of privacy and
trust in a cloud system, in [217], the authors state that both must be
weighted accordingly. A Reputation-aware Trust and Privacy scheme proposed by
[218] can deal with trust management by leveraging trust scores for cloud
services.

##### Computation in Untrusted Environments

Since outsourcing spatial keyword searches to an untrusted cloud can cause
privacy concerns, in [219], the authors proposed that top-k spatial keyword
searches can be performed instead, where spatial and textual data are
encrypted in a unified way. When an untrusted cloud environment is to be used,
a privacy-preserving framework using homomorphic cryptography outlined by
[220] can be used to conduct the needed computations.

####  3.4.2 Attack Resilience

Wireless sensor networks are often deployed in open and large areas which
makes them vulnerable to attack. To protect the location of the source node,
the authors of [221] describe how fake traffic can be created to camouflage
the source node in the cloud. If the guest operating system or the virtual
machine monitor of a cloud vendor are compromised, an attack can be launched
against users. A solution to this problem put forward by [222] is to integrate
on-chip memory protection for trusted applications from attacks.

####  3.4.3 Forensics

The aim of cloud forensics is to process large sets of data from the cloud and
recover evidence. A multigranularity privacy leakage forensics method put
forward by the authors of [223] allows for analysis of privacy violations
which have been caused by malware.

##  4 Life Cycle

The Life Cycle of a Privacy-Preserving Cloud system can be broken down into
four stages. The first subsection covers Design, which outlines what factors
must be taken into account when designing a system. Verification is covered in
the second subsection and Implementation is covered in the third. The fourth
and final subsection looks at how a system of this nature would be deployed
and evaluated.

Figure 3: Design, Verification, Implementation, and Deployment form the Life
Cycle of a Privacy-Preserving Cloud System.

In Figure 3, the formation of a Life Cycle of a Privacy-Preserving Cloud
System is illustrated, with each of the four steps in sequence. The arrows
connecting each box signifies the order in which the steps occur.

###  4.1 Design

####  4.1.1 Additional Design Objectives

##### Cost-Effectivity

Because encrypting all intermediate data sets stored on the cloud would not be
cost-effective,  the authors in [224] suggest that it is more efficient to
first identify which data sets require encrypting so that data privacy is
maintained while keeping costs low.

##### Efficiency

In [225], it is said that location-based services found in wireless
communications present new efficient schemes in outsourced cloud where the
users identity is masked for privacy purposes. By designing a data sharing
process which provides integrity by design, in [226] ,authors state that
efficiency can be improved by have a third party audit data stored on the
cloud.

##### Resource-Constraint Awareness

Vehicular cloud computing consists of many computations that must be performed
dynamically [227], but the limited computational capabilities of a single
vehicle must be taken into account. To create a functioning system, the
authors of [228] insist that a lightweight architecture must be designed.
Likewise, in the healthcare system, heavy computations must be performed on
limited hardware, necessitating the use of a lightweight resource-aware
system, such as the one proposed by [229]. A lightweight Delegatable Proofs of
Storage scheme proposed by the authors of [230] can also be used to speed up
data integrity checks in cloud storage systems.

##### Time Constraint Awareness

Because cloud storage can contain a large number of images, it is important
for security and privacy reasons that data is audited as soon as possible. For
this reason, the authors of [231], state that it is important that cloud
systems employ auditing schemes that are aware of time constraints.

##### Reliability

Cloud storage services have become a very popular way to store data, but there
are risks of data loss and data leaks. The authors of [232] demand that these
issues must be addressed in order for a cloud storage service to be reliable.

##### Scalability

Because cloud services are used to support big data applications in sectors
such as business and healthcare, it is important that they have a scalable
infrastructure. A proximity privacy model proposed by [233] can be used to
cluster data so that high scalability can be achieved by performing data-
parallel computations.

##### Performance

One way to improve the performance of a cloud system is to exploit geo-
distributed clouds to reduce service delay in a method proposed by the authors
of [234]. Algorithms such as PBtree traversal width minimization and PBtree
traversal depth minimization can also be used to increase performance by
increasing the efficiency of query processing when used as outlined by [235].

###  4.2 Verification

In order to address the risk of private data leaking to the public, a
Structural Integrity Verification method described by [236] can be applied to
cloud systems using three processes: proof organization, proof transformation,
and integrity judgement. For large sets of mobile data, the batch verification
method proposed by [237] can be used to reduce the computational cost.

###  4.3 Implementation

###  4.4 Deployment

When an organization chooses to use a cloud service, they must consider the
security and privacy risks which they are willing to take. For this reason,
the authors of [238] state that it is important that a cloud service based on
an organizations needs in these areas.

####  4.4.1 Evaluation

Using advancements in deep learning technology, the authors of [239] state
that attacks against cloud systems can be analyzed in order to propose new
defense methodologies. In [240], authors emphasize that it is important that
quantitative assessment of the privacy practises of cloud services are
analyzed so that clients can make an informed decision about which provided
they choose to do business with.

##  5 Layered Architecture

he architecture of a cloud system is layered, with each layer having its own
purpose. The first subsection examines the infrastructure of a cloud system
which maintains privacy. The second subsection looks at how privacy is
maintained with services such as pre-diagnosis computing and location-based
services. The third section details how tasks can be managed privately using
task allocation and computation offloading.

###  5.1 Privacy-Preserving Infrastructure

####  5.1.1 Privacy-Preserving Physical Layer

The authors of [241] state that in order to audit cloud data to check for data
integrity, two lightweight auditing protocols can be used based on online and
offline signatures. A device needs only to perform a computation when an
outsourced file is available.

####  5.1.2 Privacy-Preserving Data and Data Infrastructure

##### Privacy-Preserving Data

As defined by [242], Data Deduplication is a technique which eliminates
duplicate data stored on the cloud to save space and bandwidth. However, an
attacker can abuse a deduplication check to determine if a file is already
stored in a cloud system. Restricting deduplication checks to only the agent
and the cloud service provider, data privacy can be improved, according to
[27].

##### Privacy-Preserving Storage Storage

As outlined in [243], cloud storage systems allow for data to be outsourced
flexibly and conveniently. The authors of [244] detail how deduplication
allows for cloud storage to only need to keep one copy of a given file to
reduce space usage. Because data such as power meter readings require privacy,
schemes such as privacy-friendly cloud storage as detailed in [245] are
required. Privacy and reliability in cloud storage environments can be
achieved using encryption schemes proposed by the authors of [246] such as BP-
XOR secret sharing or LDPC secret sharing. Another way to preserve privacy
proposed by [247] is to enable cloud users to distribute and search encrypted
cloud data across clouds managed by CSPs. To safely share data, the authors in
[248], say that sanitizable signatures can be used to hide sensitive
information. Its is important that auditing is available in cloud storage
systems so that users can have the integrity of their data ensured, as per
[249]. In order to properly audit regenerating-based cloud storage, a proxy
that is privileged to regenerate authenticators must be used in a way detailed
in [250]. To protect cloud servers from internal attacks, a three-layer
storage framework devised by [251] can be used based on fog computing.

##### Privacy-Preserving Databases

In [28], it is outlined why multi-dimensional range queries on cloud networks
should not be fully trusted as many implementations suffer from data leakage.
A blockchain enabled query framework can result in high efficiency and
scalability [252]. For health data, a query scheme put forth by [253] could be
used where medical data is encrypted and stored on the cloud, and then
specific diseases are queried on that data. Searchable symmetric encryption,
as proposed by [254], can be used to protect the privacy of both the queries
and the database. For a secure database, a two-cloud architecture devised by
[255] can also be used, where a series of intersection protocols provide
privacy for numeric-related range queries.

####  5.1.3 Privacy-Preserving Data Exchange Infrastructure

##### Privacy-Preserving Data Sharing

Data sharing is a service that, when supplied by cloud computing, the authors
of [256] say can be convenient and economic to a user. In [257], authors
emphasize that it is important that the data being shared is not exposed to
unauthorized users or to the cloud provider in order to maintain privacy,
which generally comes with a computational cost as said in [258]. Secret
sharing group key management protocol and public auditing can be used to help
tighten security when data sharing in methods detailed by the authors of [259]
and [260]. Ring signatures can help to compute verification metadata when
performing a data audit when used as described by [261], and a broadcast group
key management system detailed by [262] can help with selectively sharing
documents.

##### Privacy-Preserving Data Publishing

With cloud computing, because data owners are no longer in possession of
physical copies of their own data, this creates the issue of privacy-
preserving set-valued data publishing as said in [263]. Using retrievable data
perturbation as detailed by the authors of [264], we can create a solution to
this issue.

####  5.1.4 Privacy-Preservig Communications

To deal with the growing amount of network traffic, some traffic is redirected
to outsourced middleboxes. To ensure data privacy during redirection,
encrypted header-matching schemes proposed by [265] can be used to keep data
safe. Network security can also be enhanced by protecting transmitted social
messages with integrated security-based mobility prediction algorithms to help
resist attacks, as detailed in [266].

####  5.1.5 Privacy-Preserving Protocols

When ensuring security during data transmission and storage, there should be
protocols set in place as describer in [267] to protect the identify of
communicating units during encryption. To evaluate a protocol, the author in
[268] state that a simulation can be performed based on typical circumstances
of attacks.

###  5.2 Privacy-Preserving Services

In the mobile healthcare field, a Diverse Keyword Searchable Encryption scheme
proposed by [269] can be used to search and make diagnosis over stored
encrypted data. To maintain privacy of genomic data, the authors of [270]
state that information should only be access through a fine-grained access
control policy with attribute-based encryption. To preserve the privacy of
location-based services, data outsourced to the cloud should be encrypted and
only be able to be queried by registered authorized users in methods detailed
by the authors of [271] and [272]. For data sharing services, the authors of
[273] state  that data should not be shared with a third-party in order to
maintain privacy.

###  5.3 Privacy-Preserving Computing

####  5.3.1 Privacy-Preserving Task Management

##### Privacy-Preserving Task allocation

In a mobile network, an auction-based privacy-preserving incentive scheme
proposed in [274] can be used for task allocation, where each user can control
their privacy budget. Using differential privacy and geocast, we can create
task allocation strategies that consider privacy, utility, and overhead in a
mobile environment as detailed in [275].

##### Privacy-Preserving computation Offloading

Computation offloading allows for devices to offload tasks to the cloud in
order to preserve energy and time. When offloading, usually devices will do so
to the nearest edge cloud which can expose the location of the device. This
issue can be resolved with a location privacy-guaranteed offloading algorithm
proposed in [276].

####  5.3.2 Privacy-Preserving Task

##### Privacy-Preserving Search

  * •

Keyword Search

Many approaches towards searching over encrypted data are only able to perform
exact searches, as per [277]. A privacy preserving ranked multi-keyword search
in a multi-owner model as proposed in [278] allows for a user to search
without knowing keyword data. With multi-keyword search, a tree-based
structure index can improve efficiency, as outlined in [279] and [280].

  * •

Content Search

A privacy-preserving image search system on cloud as outlined in [281] would
help protect users privacy by guarding the sensitive information that is
stored in image files such as location and event.

##### Privacy-Preserving Data Analytics

Privacy-preserving data classification techniques such as the ones in [282]
can be used where knowledge about client input data is not made available to
the cloud server. In order to provide for the needs of elderly people in need
of housing, in [283] the authors say that real estate recommendations can be
made based on the behaviour of the elderly in the cloud. By analyzing sports
records, [284] says that exercise recommendations can be made for individuals
who wish to live healthier. Likewise, a clinical cloud system could use data
to make diagnoses, as outlined in [285]. Since the client cannot directly
access this data, decision tree training and k-nearest neighbor computation
can be used as proposed in [286] and [287].

##### Privacy-Preserving Content Processing

  * •

Image

    * –

Image Retrieval

Cloud computing has allowed for large amounts of images to be stored easily as
outlined in [288], but this has led to a number of privacy concerns, as per
[289]. In [290], it is described how content-based image retrieval
applications have generally been limited by computation and storage
requirements. A polyalphabetic cipher can be used to improve security with no
retrieval accuracy degradation, according to [291], and k-ranked multi-keyword
search defend against keyword guessing attacks when used as outlined in [292].
In [293], the authors outline how watermark protocols can also be used to
prevent from unauthorized image copying.

    * –

Image Reconstruction In [294], it is outlined how an outsourced image recovery
service allows for users to only need to outsource compressed images to the
cloud in order to reduce the amount of storage that they use. The cloud can
then reconstruct the compressed images safely.

    * –

Image Denoising

While image denoising can help to restore an image, [295] claims that it also
raises security and privacy concerns. One such denoising method enable
encrypted databases to provide query-based image denoising.

  * •

Video

    * –

Video Surveillence During video surveillance, it is important that privacy is
protected without interfering with the regular tasks of the surveillance, say
the authors of [296]. The authors of [297] describe how Privacy Region
Protecting protects privacy regions without effecting the non-privacy regions
so that they are visually intact.

    * –

Video Reporting

In [298] it is described how in vehicular networks, real-time video reporting
can instantly report traffic accidents for safety reasons. Using a 5G network,
it can be ensured that the system is reliable and secure.

##### Privacy-Preserving Cloud Auditing

Problems such as privacy disclosure, authority abuse, and collusion attacks
can be solved by cloud auditing as detailed in [299, 300]. One such scheme
proposed in [301] allows for user anonymity without group signatures so that
tags are compact. As a real world example, unmanned aerial vehicles require an
audit scheme to accommodate their limited storage and computing performance.
They can use an audit scheme such as the one in [302] using a distributed
string equality check and Merkle-hash tree multi-level index structure.

##### Other Kinds of Privacy-Preserving Computing

Cloud computing has been used in other such ways, as detailed in [303], such
as Pockit, a calculation toolkit which outsources data to perform common
arithmetic securely. In [304], authors claim that a secure and efficient
privacy preserving provable data possession scheme can be used to allow
auditors to verify data possession. Vehicular ad hoc networks allow vehicles
to make real time computations and receive updates on things such as road
conditions and accidents as outlined in [305]. Finally, as described in [306],
cloud systems have allowed for medical prediagnosis services to develop so
that users can privately receive remote healthcare.

Figure 4: The Layered Architecture of Privacy-Preserving Cloud

In Figure 4, the three large blue parallelograms represent the three main
layers of the Layered Architecture. Within these layers are the sublayers
represented by the orange parallelograms. Withing the sublayers are the
subsublayers represented by the green parallelograms. On the left of the image
is a legend which labels the symbols and what they represent.

##  6 Future Roadmap: the Promise of AI

We anticipate that research on privacy-preserving cloud will move towards
quantum-inspired and bio-inspired AI-assisted privacy-preserving cloud in the
near future. Our reason for such an anticipation is the existence of trends
towards AI-assisted cloud, AI-assisted secure cloud and AI-assisted privacy-
preserving cloud as well as quantum-inspired AI and bio-inspired AI, which are
discussed in Subsections 6.1, 6.2, 6.3, 6.4 and 6.5, respectively.

###  6.1 AI in Cloud

AI models have been used in the vast amount of data the needs to be analyzed,
these models can learn from the data that we have provided for training then
works with a meager error rate [307].  Machine Learning and Probabilistic
Analysis based Model (MLPAM) is a novel model created with machine learning to
preserve data in the Cloud environment. A comprehensive analysis indicates its
security and efficiency in [308], the authors have argued that MLPAM could
provide safety and efficiency of data sharing and control in the future for
multiple environments such as IoT while in [309], An Automatic Cloud Detection
neural network (ACD net) has been presented that contains two features which
can overcome the overestimation problems in Cloud Computing. However, the
authors have not created a future road map and explain that their solution
cannot overcome the thin cloud detection issue because of an inadequate
training dataset. In addition, the Asynchronous-Advantage-Actor-Critic (A3C)
presented in [310] for decentralized learning to analyze the parameters of
tasks and hosts to make a scheduling decision to perform better, as well as
they introduced scalable reinforcement learning models that can be explored
more in the future. Furthermore, for developing and managing Deep Neural
Network (DNN) in the Cloud-to-Things Continuum, the authors in [311] have used
Kafka-ML framework, an open-source framework used in Machine Learning and
Artificial Intelligence management. Their analysis has indicated that response
time has improved noticeably in comparison with Cloud-only deployment.
Moreover, Since the mobile web has not had adequate resources to support Deep
Neural Network (DNN) in cloud computing, the authors in [312] a Lightweight
Collaborative Deep Neural Network called LcDNN has been presented in order to
make use of Deep Neural Network (DNN) possible in the cloud for mobile.
However, a new deep model named Multiscale Attention Convolutional Neural
Network (MACNN) has been presented in In [313], that used asymmetric encoder-
decoder structure for ground-based cloud detection. In contrast, in [314], a
utility-based mechanism has been offered to equilibrium cost and availability
in spot instances. Likewise, a Long Short Term Memory for Neural network
framework has been used to examine price tendency. On the other hand, a method
in neural network architecture named CloudU-Net for Ground-based Daytime and
Nighttime Cloud Images (GDNCI) used has been presented in [315], that is used
in the weather forecast; as observed in the paper, its performances have been
improved for daytime and nighttime cloud images. As opposed to [316] that An
innovative Multilevel Attention-based U-shape Graph Neural Network (MAUGNN)
has been introduced, and the authors’ purpose was using (MAUGNN) is to learn
point cloud effectively due to 3-D sensors in the IoT industry. In addition,
in [317] a multiscale convolutional neural network with color vegetation
indices (MCCNN) has been presented. The purpose of this model is labeling in
point cloud for the 3-D model; also, the authors have analyzed its efficiency
in Terrestrial Laser Scanning (TLS) data ( Semantic3D) and ALS data which is
Vaihingen3D data point cloud. Similar to [318] that a Pipelined point cloud
based Neural Network processor (PNN) has been presented in order to low
latency PNN-based 3-D intelligent systems in mobile devices. The authors in
[318] have claimed the speed of algorithms has increased because of the 3-D
points from the depth image sample and group directly. Furthermore, a novel
spatiotemporal neural network with four modules has been introduced in order
to remove cloud cover from satellite images in [319] not to mention its impact
on the quality of remote sensing mapping and geoinformation has gotten better.
As opposed to [320] that Neural Ordinary Differential Equations (NODEs) with
Recurrent Neural Networks (RNNs) has been studied in order to assimilate image
observations that are to say, with this method classifying missing data in
cloud cover is possible. In [321] similarly, a new model was presented for
detecting cloud, the method has been developed in Deep Neural Network (DNN)
[322] technique with the cross-track infrared sounder, and authors have used
the cloud information measured from the image to train their model. Moreover,
a number of methods in the neural network, which can be in time-series dataset
of cloud server power modeling, have been analyzed in [323], also, the authors
have studied the advantages and drawbacks of models and their effects. In
contrast, deep neural networks have been presented in [324] to obtain deep-
hidden fault information in cloud data centers by analyzing massive alarm data
collected in their interconnections. In addition, due to storing and analyzing
an illimitable amount of data is just possible in cloud computing, in [325], a
method for predicting the amount of data requisite cloud services has been
presented that is a hierarchical Pythagorean fuzzy deep neural network.
Furthermore, in order to improve the accuracy of occupancy map videos, the
authors in [326] implement a Convolutional Neural Network (CNN). They claimed
that this is the first learning-based work to improve the performance of
Videobased point Cloud Compression (V-PCC).

###  6.2 AI in Secure Cloud

A federated-learning-enabled IoT scheme that provides security and efficiency
has been presented in [327], this framework has been produced in order to
provide communication-efficient and privacy-preserving users’ data in smart
grids with Cloud collaborations similar to [328] that a secure, verifiable,
and fair technique has been presented which protect the privacy of sensitive
information, and it operates in an untrustworthy cloud-server. Furthermore, in
order to preserve medical diagnosis from Data Islands(DI)-level poisoning
attacks, a secure federated learning mechanism that guarantees the
confidentiality of DI-related information named SFAP that make use of multiple
keys possible has been presented in[329]. In contrast with [330] that authors
have analyzed several neural network prediction services from the perspective
of privacy-preserving, the privacy of the model, and the query are considered
as well as examined several proposals and introduced an optimized neural
network prediction scheme that brings about high accuracy, model privacy, and
low overheads in the outsourcing setting. Moreover, In [331], different
features have been created to symbolize each disability, then by these
features, the image of people with a disability such as wheelchairs, blind
people, and people with Down syndrome have been classified. The authors’
result in [331] has demonstrated that the level of securing image mobility in
Cloud systems has been improved. In addition, the authors have introduced a
framwork that provides training and testing data privacy in [332], this
framwork is a secure cloud-intelligent network that is supported by privacy-
preserving machine learning similar to [333] that authors have introduced a
framwork called SPDDL (Secure and Privacy-preserving distributed deep
learning) for secure and privacy-preserving Distributed Deep Learning (DDL)
that makes better security, efficiency, and functionality and preserves users
identities from external adversaries. Likewise, a framwork that is secure and
provides authentication for E-Education (online and virtual education) in
cloud systems has been presented in [334] to overcome cost, time processing,
and security challenges. As opposed to [335] that authors have indicated how
to make use of several software-as-a-service (SaaS) on a platform-as-a-service
(PaaS) by analyzing data security also the integration between machine
learning algorithms and secure data protection algorithms has been studied in
[335] similar to [336] that the authors produced and introduced a Phishing
Detection (PD) framework that has a three-step feature and developed in
machine learning and a Secure Storage Distribution (SSD) in order to provide
security and privacy Cloud-IoT data. In addition, a novel firewall method
called Enhanced Intrusion Detection and Classification (EIDC) has been
introduced in [337]. This firewall detects and ranks by a technique named most
frequent decision that increases the performance of learning as opposed to
[338] that a model of Quality of Services (QoS) has been introduced that is
new and intelligent. The critical feature in that model is deciding the files
to be decrypted without decrypting the whole file list in the secure Storage.
One of the most crucial concerns is balancing security and performance, which
affect each other in the cloud user community. In deep learning networks on a
bare metal cloud-based, a system called License Plate Recognition System
(LPRS) has been constructed and introduced in [339]. the purposes of this
system are plate localization, character detection, and segmentation as
opposed to [340] that authors have tried to improve the speed and accuracy of
facial recognition without losing the privacy of robots in the encrypted
domain. Similar to [341] that a model of cloud data repository has been
introduced that has trained with mobile data dataset through  Training dataset
Filtration Key Nearest Neighbor (TsF-KNN) and public-key cryptographic
algorithms. The algoritms decline the time of the process and provide privacy
and integrity of data. Furthermore, the authors in [342] have argued the
advantages of M-Learning by utilizing Mobile cloud computing and analyzed
issues and security. Also, they have provided an introduction of Mobile cloud
computing structures. Likewise, the authors in [343] have developed a face
recognition that is secure in cloud computing in order to protect privacy by
random unitary transform encryption algorithms and for dealing with noise and
modeling error by making use of multi-device diversity.

###  6.3 AI in Privacy-Preserving Cloud

Although federated learning has been designed to provide data privacy, the
data uploaded from federated learning has been threatened by attackers [344,
345]. In [346], authors have introduced an innovative mechanism to preserve
data privacy on cloud-edge learning systems similar to [347] that the authors
have introduced an encryption technique called BGV that encrypts data and
makes use of cloud servers algorithms to train for the deep computation model.
Furthermore, the secure-centralized-computation-privacy-preserving
reinforcement learning algorithm has been presented in [348] in order to
protect the privacy of users’ data by using a fully homomorphic encryption
scheme in a cloud computing infrastructure. In order to provide better
complexities and security, a deep neural network architecture has been
introduced in [349], called MSCryptoNet that works based on a fully
homomorphic cryptosystem in the privacy-preservation setting. Nevertheless,
there is a growing concern about the privacy and security of data when it
comes to machine learning. In [350], the authors have introduced a secure
cloud-intelligent network framework that preserves the privacy of data as
opposed to [351] that authors have produced a framwork that organizes sparse
coding in edge and cloud networks. In this framework for recognizing noise and
error of data, they have utilized classification. Moreover, one of the most
popular applications in the fog-cloud computing environment is distributed
deep learning (DDL) because of its efficiency and scalability. However, there
are challenges when using DLL in fog-cloud computing, such as protecting
users’ privacy in the training process and confirming users’ identities forged
by external adversaries. According to those problems, the authors in [352]
have presented a secure and privacy-preserving DDL (SPDDL) for fog-cloud
computing. In addition, one of the challenges in the internet of things (IoT)
is big data that have been generated. A double-projection deep computation
model (DPDCM) for feature learning in big data has been introduced in [353],
which has two layers. In addition, For training the DPDCM, the authors have
designed a learning algorithm. However, our result in neural network learning
depends on how much data we use in the training step. If we want to use
another party’s data set, we need a solution because no party wants to
disclose people’s private data to others. In [354], authors have described
their practical multiparty Back-Propagation neural network learning scheme
over arbitrarily partitioned data. In this model, the Cloud runs most
operations on encrypted data from ciphertext uploaded. Whereas In [355], the
authors have argued the privacy of users’ data which is used to train. They
introduced a model that encrypts users’ data with their public key called the
privacy-preserving deep learning model. Furthermore, in smart cities where
urban facilities use technology such as smart health, parking, transport, We
are dealing with the date which its privacy and security are vital. In [356],
the authors introduced a privacy-preserving autoencoder-based deep learning
classifier in the Cloud for solving this problem in smart cities. In addition,
Homomorphic Secure Multi-party Computation (SMC) or Hmomorphic Encryption (HE)
schemes have been used for the security of the cloud process with considering
the privacy of data. The authors in [357] have argued supervised and
unsupervised machine learning capability through neural networks on encrypted
data similar to [358] that for privacy-preserving deep neural networks,
authors have introduced a framwork for transformation network training in
coordination. By using the framework, users can train a transformation network
with a model from a cloud provider in a secure environment. Moreover, for
analyzing big data, at first, organizations should remove Personally
Identifiable Information (PII) because it breaks users’ privacy. A Mondrian-
based k-anonymity and Deep Neural Network (DNN) based framework has been
presented in [359] to preserve data privacy; their analysis shows that it was
suitable similar to [360] that in DNN training/classification a new algorithm
was presented that filtered images in order to preserve cloud base privacy. in
addition, the authors have presented a cloud-based federated approach that
makes use of multiple clouds with different distorted data sets and
demonstrated appropriate accuracy of DNN with the original dataset can be
provided. In contrast, a technique that analyzes images on the client devices
and is independent of local Convolutional Neural Network (CNN) has been
presented in [361]. Also, the most important effect of CNN-based applications
in the mobile Cloud that works on raw image data is serious privacy concerns.
Furthermore, In [362] the challenges in time latency, energy consumption, and
privacy level have been analyzed in an Edge-Cloud Collaboration (ECC) scenario
including several users. The authors presented a Markov Decision Process (MDP)
for balancing cost and the privacy level and the Deep Q-Network (DQN), which
decreases delay, energy cost, and improves privacy. However, In the cloud
environment, data protection is a vital step. In [363] a Machine Learning and
Probabilistic Analysis based Model (MLPAM) has been introduced in order to
provide security and data protection in the cloud environment. In addition,
For reducing data leakage, MLPAM has a powerful sharing protocol. To that end,
a growing number of organizations have utilized machine learning applications
for many purposes, such as attack detection. We can make sure of its accuracy
and efficiency with a privacy-aware deployment method (PDM) that has been
presented in [364]. With this method, we can provide privacy which is a
challenge in cyber-physical cloud systems (CPCSs).

###  6.4 Quantum-Inspired AI

Processing a vast amount of dataset that is complex requires evolving
algorithms to provide a way for better training and learning. Since The
trajectory planning problem is one of the challenges in a wireless uplink
transmission scenario, the authors in[365] have presented a solution that is
optimizing via the quantum-inspired reinforcement learning (QiRL) method that
improves the performance of an uncrewed aerial vehicle from when it starts
flying to its destination. Moreover, New training algorithms have been
presented for deep reinforcement learning (DRL) to equilibrium exploration and
exploitation in [366]. As opposed to the traditional mechanism, this algorithm
is inspired by quantum computation. Also, in [367] quantum-inspired
multidirectional association model called QMAM and a one-shot learning and
self-convergent iterative learning called IQMAM. Their analysis has indicated
the ability to maintain a stable memory capacity and reliable recall in the
model. In contrast, in [368], autonomous mobile robots have been controlled
navigationally by A new Quantum-inspired Reinforcement Learning (QiRL) method.
According to Markovian tests, the QiRL method is better than traditional
reinforcement learning in learning and initial states. Likewise, In[369],
authors have been presented Quantum-inspired Fuzzy Based Neural Network
(Q-FNN) model that is new in learning for challenging two-class
classification. This model is much better for accuracy, sensitivity, and
specificity.

###  6.5 Bio-inspired AI

In [370], bio-inspired representation learning is said to combine low-level
contrast and high-level semantic features in order to generate a visual
attention map. As outlined in [371], a neural network can be used to train
Bio-inspired features. Some such examples can be seen in [372] where bio-
inspired technologies are used to simulate animal movements in aerial
vehicles, and in [373] where it is used for autonomous navigation. In [374],
bio-inspired metaheuristic algorithms are used for the detection of spam
emails and a bio-inspired algorithm in [375] is used for optimizing the
Internet of Things. Another optimization algorithm can be found in [376] where
it is used for UAV planning.

Figure 5: Bioscience and Quantum Join AI-assisted Privacy Preserving Cloud to
Create Bio-inspired and Quantum-inspired AI-assisted privacy-preserving cloud.

In Figure 5, the left side of the figure represents the current state of
Privacy-Preserving Cloud technology, whereas the right side shows the future
for this technology. The arrows from Privacy-Preserving Cloud point towards
other technologies with which it can join to create new technologies. The DNA
symbol represents Bio Science. Privacy-Preserving Cloud joins with Bio Science
to create Bio-Inspired Privacy-Preserving Cloud. The Atom symbol represents
Quantum Science, which joins with Privacy-Preserving Cloud to create Quantum-
Inspired Privacy-Preserving Cloud.

##  7 Conclusion

Over the course of this survey paper, we have highlighted the Ecosystem,
Applications, Architecture, and Lifecycle of Privacy-Preserving Cloud
Computing. By creating this survey paper, future researchers who would like to
learn more about Privacy-Preserving Cloud can now use this paper as a resource
to strengthen their knowledge of the subject. Because of the benefits that
Cloud Computing has provided, it is used in many fields. However, because of
the high level of usage of Cloud Computing across so many industries and
technologies, there is a large amount of sensitive data that is potentially
exposed. Paying attention to this subject, there is a growing concern about
privacy in Cloud Computing. Having looked at this issue, Privacy-Preserving
Cloud Computing technologies and methods have emerged in order to provide
security and privacy. To recap the main points, current surveys from 2010 have
been reviewed, and the summary of them has been provided in the tables that
can be found at the end of Section Two. Those tables indicate whether the
survey focuses on privacy or draws a future roadmap or the cloud computing has
been reviewed in a specific application. Furthermore, Design, Verification,
Implementation, and Evaluation that are categorized as the four stages of the
Life Cycle of the Privacy-Preserving Cloud system have been reviewed. The
layered of a cloud computing system is also detailed in order to explain the
systems that work together in order to make sure that a cloud system can stay
private. Moreover, the benefits of Privacy-Preserving Cloud Computing have
been observed in the ecosystem. The future road map that consists Quantum-
Inspired AI and Bio-Inspired AI have been presented in this paper. We would
encourage researchers wishing to continue our work to study these areas
further as the technology develops in the future. Using these new fields, the
ways in which cloud computing can be used as a tool can increase in even
greater numbers.

## References

  * [1] S. Tang, C. Yu, and Y. Li, “Fairness-efficiency scheduling for cloud computing with soft fairness guarantees,” _IEEE Transactions on Cloud Computing (Early Access Article)_ , 2020.
  * [2] J. Lai, F. Guo, W. Susilo, X. Huang, P. Jiang, and F. Zhang, “Data access control in cloud computing: Flexible and receiver extendable,” _IEEE Transactions on Services Computing (Early Access Article)_ , 2021.
  * [3] Z. Yang, B. Liang, and W. Ji, “An intelligent end-edge-cloud architecture for visual iot assisted healthcare systems,” _IEEE Internet of Things Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [4] Y. Zhang, Y. Sun, Y. Sun, R. Jin, K. Lin, K. Lin, W. Liu, and W. Liu, “High-performance isolation computing technology for smart iot healthcare in cloud environments,” _IEEE Internet of Things Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [5] M. Papa, V. Mattioli, M. Montopoli, D. Casella, B. Rydberg, and F. S. Marzano, “Investigating spaceborne millimeter-wave ice cloud imager geolocation using landmark targets and frequency-scaling approach,” _IEEE Transactions on Geoscience and Remote Sensing (Early Access Article)_ , pp. 1–1, 2021.
  * [6] M. Pan, Y. Li, Z.-L. Zhang, and J. Luo, “Sccs: Smart cloud commuting system with shared autonomous vehicles,” _IEEE Transactions on Big Data (Early Access Article)_ , pp. 1–1, 2020.
  * [7] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, H. Karimipour, G. Srivastava, and M. Aledhari, “Enabling drones in the internet of things with decentralized blockchain-based security,” _IEEE Internet of Things Journal_ , vol. 8, no. 8, pp. 6406–6415, 2020.
  * [8] Y. Zhang and H.-Y. Wei, “Risk-aware cloud-edge computing framework for delay-sensitive industrial iots,” _IEEE Transactions on Network and Service Management (Early Access Article)_ , pp. 1–1, 2021.
  * [9] I. M. Ali, K. M. Sallam, N. Moustafa, R. Chakraborty, M. J. Ryan, and K.-K. R. Choo, “An automated task scheduling model using non-dominated sorting genetic algorithm ii for fog-cloud systems,” _IEEE Transactions on Cloud Computing (Early Access Article)_ , pp. 1–1, 2021.
  * [10] M. Aazam, S. U. Islam, S. T. Lone, and A. Abbas, “Cloud of things (cot): Cloud-fog-iot task offloading for sustainable internet of things,” _IEEE Transactions on Sustainable Computing (Early Access Article)_ , pp. 1–1, 2020.
  * [11] R. M. A. H. ur rehman, M. Liaqat, A. H. M. Aman, S. H. A. Hamid, R. L. Ali, J. Shuja, and M. K. Khan, “Sensor cloud frameworks: State-of-the-art, taxonomy, and research issues,” _IEEE Sensors Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [12] X. Jiang, J. Ni, J. Wu, and X. Yang, “A cloud big-data-driven dynamics control approach for unmanned ground vehicles for safety improving,” _IEEE Intelligent Transportation Systems Magazine (Early Access Article)_ , pp. 1–1, 2021.
  * [13] X. Wang, C. Xu, K. Wang, F. Yan, and D. Zhao, “Memory scaling of cloud-based big data systems: A hybrid approach,” _IEEE Transactions on Big Data (Early Access Article)_ , pp. 1–1, 2020.
  * [14] T. S. Chang and T.-H. Chen, “Rangeseg: Range-aware real time segmentation of 3d lidar point clouds,” _IEEE Transactions on Intelligent Vehicles (Early Access Article)_ , pp. 1–1, 2021.
  * [15] P. Zhao and G. Dan, “Joint resource dimensioning and placement for dependable virtualized services in mobile edge clouds,” _IEEE Transactions on Mobile Computing (Early Access Article)_ , pp. 1–1, 2021.
  * [16] T. Sun, G. Liu, R. Li, S. Liu, S. Zhu, and B. Zeng, “Quadratic terms based point-to-surface 3d representation for deep learning of point cloud,” _IEEE Transactions on Circuits and Systems for Video Technology (Early Access Article)_ , pp. 1–1, 2021.
  * [17] Z. Su, Y. Wang, T. Luan, N. Zhang, F. Li, T. Chen, and H. Cao, “Secure and efficient federated learning for smart grid with edge-cloud collaboration,” _IEEE Transactions on Industrial Informatics (Early Access Article)_ , pp. 1–1, 2021.
  * [18] G. J. Portella, G. N. Rodrigues, E. Y. Nakano, A. Boukerche, and A. C. M. Melo, “A novel statistical and neural network combined approach for the cloud spot market,” _IEEE Transactions on Cloud Computing (Early Access Article)_ , pp. 1–1, 2021.
  * [19] M. S. Al-Abiad, A. Douik, S. Sorour, and J. Hossain, “Throughput maximization in cloud-radio access networks using cross-layer network coding,” _IEEE Transactions on Mobile Computing (Early Access Article)_ , pp. 1–1, 2020.
  * [20] X. Tang, “Reliability-aware cost-efficient scientific workflows scheduling strategy on multi-cloud systems,” _IEEE Transactions on Cloud Computing (Early Access Article)_ , pp. 1–1, 2021.
  * [21] B. Ray, A. Saha, S. Khatua, and S. Roy, “Proactive fault-tolerance technique to enhance reliability of cloud service in cloud federation environment,” _IEEE Transactions on Cloud Computing (Early Access Article)_ , pp. 1–1, 2020\.
  * [22] C. Zhao, J. S. Gill, P. Pisu, and G. Comert, “Detection of false data injection attack in connected and automated vehicles via cloud-based sandboxing,” _IEEE Transactions on Intelligent Transportation Systems (Early Access Article)_ , pp. 1–1, 2021.
  * [23] A. Yazdinejad, A. Dehghantanha, R. M. Parizi, M. Hammoudeh, H. Karimipour, and G. Srivastava, “Block hunter: Federated learning for cyber threat hunting in blockchain-based iiot networks,” _IEEE Transactions on Industrial Informatics_ , pp. 1–1, 2022.
  * [24] D. Cotroneo, L. D. Simone, P. Liguori, and R. Natella, “Fault injection analytics: A novel approach to discover failure modes in cloud-computing systems,” _IEEE Transactions on Dependable and Secure Computing (Early Access Article)_ , pp. 1–1, 2020.
  * [25] Y. Zhang, C. Xu, N. Cheng, and X. S. Shen, “Secure password-protected encryption key for deduplicated cloud storage systems,” _IEEE Transactions on Dependable and Secure Computing (Early Access Article)_ , pp. 1–1, 2021.
  * [26] H. Xiong, X. Huang, M. Yang, L. Wang, and S. Yu, “Unbounded and efficient revocable attribute-based encryption with adaptive security for cloud-assisted internet of things,” _IEEE Internet of Things Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [27] A. Sultangazin and P. Tabuada, “Symmetries and isomorphisms for privacy in control over the cloud,” _IEEE Transactions on Automatic Control_ , vol. 66, no. 2, pp. 538–549, 2021.
  * [28] A. Alabdulatif, I. Khalil, A. Y. Zomaya, Z. Tari, and X. Yi, “Fully homomorphic based privacy-preserving distributed expectation maximization on cloud,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 31, no. 11, pp. 2668–2681, 2020.
  * [29] S. Nakhodchi, B. Zolfaghari, A. Yazdinejad, and A. Dehghantanha, “Steeleye: An application-layer attack detection and attribution model in industrial control systems using semi-deep learning,” in _2021 18th International Conference on Privacy, Security and Trust (PST)_. IEEE, 2021, pp. 1–8.
  * [30] A. Yazdinejad, G. Srivastava, R. M. Parizi, A. Dehghantanha, K.-K. R. Choo, and M. Aledhari, “Decentralized authentication of distributed patients in hospital networks using blockchain,” _IEEE journal of biomedical and health informatics_ , vol. 24, no. 8, pp. 2146–2156, 2020.
  * [31] A. Yazdinejad, R. M. Parizi, A. Bohlooli, A. Dehghantanha, and K.-K. R. Choo, “A high-performance framework for a network programmable packet processor using p4 and fpga,” _Journal of Network and Computer Applications_ , vol. 156, p. 102564, 2020.
  * [32] S. A. . M. A. S. Elameer, “A review in security issues and challenges on mobile cloud computing (mcc),” in _Proceedings of 1st Annual International Conference on Information and Sciences (AiCIS)_ , Fallujah, Iraq, November 2018.
  * [33] A. K. B. S. M, “A review on challenges of security for secure data storage in cloud,” in _Proceedings of International Conference on Smart Systems and Inventive Technology (ICSSIT)_ , Tirunelveli, India, November 2019.
  * [34] A. B. S. A.-Z. R. A.-S. A. Rodan, “Storage as a service (staas) security challenges and solutions in cloud computing environment: An evaluation review,” in _Proceedings of Sixth HCT Information Technology Trends (ITT)_ , Ras Al Khaimah, United Arab Emirates, November 2019.
  * [35] A. M. S. P. R. T. G. S. Nath, “Cloud computing security issues & challenges: A review,” in _Proceedings of International Conference on Computer Communication and Informatics (ICCCI)_ , Coimbatore, India, January 2020.
  * [36] S. S. P. S. Sudhish, “Security in cloud computing systems: A review of challenges and solutions for security in distributed computing environments,” in _Proceedings of 39th National Systems Conference (NSC)_ , Greater Noida, India, December 2015.
  * [37] A. N. D. Gupta, “A review on different security issues and challenges in cloud computing,” in _Proceedings of International Conference on Computing, Power and Communication Technologies (GUCON)_ , Greater Noida, India, September 2018.
  * [38] C. K. G. S. G. S. R. S. Batth, “Security issues and challenges in cloud computing: A mirror review,” in _Proceedings of International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)_ , Dubai, United Arab Emirates, December 2019.
  * [39] R. K. J. Kaur, “Cloud computing security issues and its solution: A review,” in _Proceedings of 2nd International Conference on Computing for Sustainable Global Development (INDIACom)_ , New Delhi, India, March 2015.
  * [40] J. G. S. M. Sharma, “Cloud computing and its security issues — a review,” in _Proceedings of Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)_ , Hefei, China, July 2014\.
  * [41] N. C. Paxton, “Cloud security: A review of current issues and proposed solutions,” in _Proceedings of IEEE 2nd International Conference on Collaboration and Internet Computing (CIC)_ , Pittsburgh, PA, USA, November 2016\.
  * [42] I. I. M. Daneva, “Cloud computing security requirements: A systematic review,” in _Proceedings of Sixth International Conference on Research Challenges in Information Science (RCIS)_ , Valencia, Spain, 2012.
  * [43] T. A. P. S. P. A. T. Bhole, “A review on contemporary security issues of cloud computing,” in _Proceedings of 1st International Conference on Intelligent Systems and Information Management (ICISIM)_ , Aurangabad, India, October 2017.
  * [44] R. D. V. Kute, “A review paper on security concerns in cloud computing and proposed security models,” in _Proceedings of International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)_ , Vellore, India, 2020.
  * [45] Y. B. Z. Y. B. Bahli, “Key topics in cloud computing security: A systematic literature review,” in _Proceedings of 2nd International Conference on Information Science and Security (ICISS)_ , Seoul, Korea (South), December 2015\.
  * [46] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, Q. Zhang, and K.-K. R. Choo, “An energy-efficient sdn controller architecture for iot networks with blockchain-based security,” _IEEE Transactions on Services Computing_ , vol. 13, no. 4, pp. 625–638, 2020.
  * [47] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, and K.-K. R. Choo, “Blockchain-enabled authentication handover with efficient privacy protection in sdn-based 5g networks,” _IEEE Transactions on Network Science and Engineering_ , 2019.
  * [48] K. L. C. C. J. G. Q. L. Y. Guo, “A review of research on security of cloud service platform in medical environment,” in _Proceedings of IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)_ , Yilan, Taiwan, May 2019.
  * [49] A. Yazdinejad, A. Dehghantanha, H. Karimipour, G. Srivastava, and R. M. Parizi, “An efficient packet parser architecture for software-defined 5g networks,” _Physical Communication_ , vol. 53, p. 101677, 2022.
  * [50] X. N. H. Suo, “Security in the cloud computing: A review,” in _Proceedings of 2012 2nd International Conference on Computer Science and Network Technology_ , Changchun, China, December 2012.
  * [51] N. P. E. D. N. V. C. Lambrinoudakis, “It’s all in the cloud: Reviewing cloud security,” in _Proceedings of IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing_ , Vietri sul Mare, Italy, December 2013.
  * [52] G. S. V. A. G. Suganya, “A comprehensive review on cloud computing security,” in _Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)_ , Coimbatore, India, March 2017.
  * [53] V. B. P. K. K. B. P. Lohani, “A review of security of the cloud computing over business with implementation,” in _Proceedings of International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH)_ , Greater Noida, India, February 2016.
  * [54] S. N. S. A. G. A. F. D. Hutchison, “The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective,” _IEEE Journal on Selected Areas in Communications_ , vol. 35, no. 11, pp. 2586–2595, 2017.
  * [55] M. M. A. K. D. G. A. K. N. S. P. A. Z. K. M. A. P. Mahmud, “Applications and evaluations of bio-inspired approaches in cloud security: A review,” _IEEE Access_ , vol. 8, pp. 180 799–180 814, 2020.
  * [56] P. J. D. R. S. Patidar, “A survey and analysis of cloud model-based security for computing secure cloud bursting and aggregation in renal environment,” in _Proceedings of World Congress on Information and Communication Technologies_ , Mumbai, India, December 2011.
  * [57] B. G. A. B. P. Haller, “A survey on cloud-based software platforms to implement secure smart grids,” in _Proceedings of 49th International Universities Power Engineering Conference (UPEC)_ , Cluj-Napoca, Romania, September 2014.
  * [58] F. H. K. S. Y. F. X. Y. X. Yi, “Online soc estimation for li-ion batteries: A survey explore the distributed secure cloud management to battery packs,” in _Proceedings of 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)_ , Siem Reap, Cambodia, June 2017.
  * [59] N. A. A. K. M. A. Shibli, “Survey on secure live virtual machine (vm) migration in cloud,” in _Proceedings of 2nd National Conference on Information Assurance (NCIA)_ , Rawalpindi, Pakistan, December 2013.
  * [60] H. S. K. Aida, “A hybrid and secure mechanism to execute parameter survey applications on local and public cloud resources,” in _Proceedings of IEEE Second International Conference on Cloud Computing Technology and Science_ , Indianapolis, IN, USA, November-December 2010.
  * [61] S. I. C. on Advanced Cloud and B. D. (CBD), “Exploration of secured workflow scheduling models in cloud environment: A survey,” in _Proceedings of Akindipe Olusegun Francis Bugingo Emmanuel Defu Zhang Wei Zheng Yingsheng Qin Dongzhan Zhang_ , Lanzhou, China, August 2018.
  * [62] A. U. P. Lakkadwala, “Secure live migration of vm’s in cloud computing: A survey,” in _Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization_ , Noida, India, October 2014\.
  * [63] P. J. Sun, “Privacy protection and data security in cloud computing: A survey, challenges, and solutions,” _IEEE Access_ , vol. 7, pp. 147 420–147 452, 2019.
  * [64] S. Chenthara, K. Ahmed, H. Wang, and F. Whittaker, “Security and privacy-preserving challenges of e-health solutions in cloud computing,” _IEEE Access_ , vol. 7, pp. 74 361–74 382, 2019.
  * [65] Z. X. Y. Xiao, “Security and privacy in cloud computing,” _IEEE Communications Surveys & Tutorials_, vol. 15, no. 2, pp. 843–859, 2013.
  * [66] P. S. E. M. P. Simões, “Privacy in the cloud: A survey of existing solutions and research challenges,” _IEEE Access_ , no. 9, pp. 10 473–10 497, 2021\.
  * [67] P. Y. N. X. J. Ren, “Data security and privacy protection for cloud storage: A survey,” _IEEE Access_ , vol. 8, pp. 131 723–131 740, 2020.
  * [68] A. M. D. S. L. S. Cho, “Security and privacy challenges in connected vehicular cloud computing,” _IEEE Communications Surveys & Tutorials_, vol. 22, no. 4, pp. 2725–2764, 2020.
  * [69] A. A. S. U. Khan, “A review on the state-of-the-art privacy-preserving approaches in the e-health clouds,” _IEEE Journal of Biomedical and Health Informatics_ , vol. 18, no. 4, pp. 1431–1441, 2014.
  * [70] A. S. D. L. Y. Li, “A review of the state of the art in privacy and security in the ehealth cloud,” _IEEE Access_ , vol. 9, pp. 104 127–104 141, 2021\.
  * [71] E. K. C. K. H. M. S. Gritzalis, “Privacy as an integral part of the implementation of cloud solutions,” _The Computer Journal_ , vol. 58, no. 10, pp. 2213–2224, 2015.
  * [72] S. H. N. K. R. A. K. K. T. Rajgopal, “Cloud architectures encountering data security and privacy concerns — a review,” in _Proceedings of International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)_ , Chennai, India, August 2017.
  * [73] U. A. S. Vinay, “A short review on data security and privacy issues in cloud computing,” in _Proceedings of IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)_ , Bangalore, India, March 2016.
  * [74] A. G. M. K. G. Mohan, “Security and privacy designs based data encryption in cloud storage and challenges: A review,” in _Proceedings of 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)_ , Bengaluru, India, July 2018.
  * [75] E. Rabieinejad, A. Yazdinejad, T. Hasani, R. M. Parizi, and M. Hammoudeh, “An efficient defi-based data warehousing approach in supply chain finance,” in _The 5th International Conference on Future Networks & Distributed Systems_, 2021, pp. 177–185.
  * [76] M. F. N. A. Hussien, “Data security implementations in cloud computing: A critical review,” in _Proceedings of 3rd International Conference on Information Technology Research (ICITR)_ , Moratuwa, Sri Lanka, December 2018.
  * [77] V. S. S. Ramamoorthy, “A review on secure data access through multi-keyword searching in cloud storage,” in _Proceedings of Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)_ , Tirunelveli, India, 2021.
  * [78] A. M. P. D. Y. Gajmal, “Data access security in cloud computing: A review,” in _Proceedings of International Conference on Computing, Power and Communication Technologies (GUCON)_ , Greater Noida, India, September 2018.
  * [79] S. G. K. P. Sharma, “A review on applying tier in multi cloud database (mcdb) for security and service availability,” in _Proceedings of International Conference on Computer Science, Engineering and Applications (ICCSEA)_ , Gunupur, India, March 2020.
  * [80] M. J. S. B. N. T. S. Prakash, “Analytical review of data security in cloud computing,” in _Proceedings of 2nd International Conference on Intelligent Engineering and Management (ICIEM)_ , London, United Kingdom, April 2021.
  * [81] N. O. O. K. P. M. L. Yang, “Data security frameworks for mobile cloud computing: A comprehensive review of the literature,” in _Proceedings of 29th International Telecommunication Networks and Applications Conference (ITNAC)_ , London, United Kingdom, November 2019.
  * [82] B. S. S. L. Vishwamitra, “Review paper on data security in cloud computing environment,” in _Proceedings of 9th International Conference System Modeling and Advancement in Research Trends (SMART)_ , Moradabad, India, December 2020.
  * [83] R. K. M. P. S. Bhatia, “A systematic review of the security in cloud computing: Data integrity, confidentiality and availability,” in _Proceedings of IEEE International Conference on Computing, Power and Communication Technologies (GUCON)_ , Greater Noida, India, October 2020.
  * [84] A. A. M. K. S. U. P. R. K. Rambola, “Effectiveness of confidentiality, integrity and availability in the security of cloud computing: A review,” in _Proceedings of 4th International Conference on Computing Communication and Automation (ICCCA)_ , Greater Noida, India, December 2018.
  * [85] A. Yazdinejad, S. Kavei, and S. Razaghi Karizno, “Increasing the performance of reactive routing protocol using the load balancing and congestion control mechanism in manet,” _Computer and Knowledge Engineering_ , vol. 2, no. 1, pp. 33–42, 2019.
  * [86] A. T. M. D. K. P. K. Singh, “A review of scalable data sharing techniques for secure cloud storage,” in _Proceedings of International Conference on Advances in Computer Engineering and Applications_ , Ghaziabad, India, March 2015\.
  * [87] V. D. V. L. Jyothi, “Survey on efficient and secure storage of diabetes patient records in cloud,” in _Proceedings of Third International Conference on Science Technology Engineering & Management (ICONSTEM)_, Chennai, India, March 2017.
  * [88] K. R. V. Dhanakoti, “Methodological survey to improve the secure data storage in cloud computing,” in _Proceedings of International Conference on Emerging Smart Computing and Informatics (ESCI)_ , Pune, India, March 2020.
  * [89] A. K. B. S. M, “A review on challenges of security for secure data storage in cloud,” in _Proceedings of International Conference on Smart Systems and Inventive Technology (ICSSIT)_ , Tirunelveli, India, November 2019.
  * [90] S. N. S. G. R. Chillarge, “A review on secure techniques for keyword-based search and data sharing in cloud computing,” in _Proceedings of Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)_ , Tirunelveli, India, 2021.
  * [91] V. S. S. Ramamoorthy, “A review on secure data access through multi-keyword searching in cloud storage,” in _Proceedings of Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)_ , Tirunelveli, India, 2021.
  * [92] A. S. K. P. G. Shidaganti, “Cloud storage security risks, practices and measures: A review,” in _Proceedings of IEEE International Conference for Innovation in Technology (INOCON)_ , Bangluru, India, November 2020.
  * [93] A. M. A. Joseph, “Blockchain for cloud storage security: A review,” in _Proceedings of 4th International Conference on Intelligent Computing and Control Systems (ICICCS)_ , Madurai, India, May 2020.
  * [94] A. B. S. A.-Z. R. A.-S. A. Rodan, “Storage as a service (staas) security challenges and solutions in cloud computing environment: An evaluation review,” in _Proceedings of Sixth HCT Information Technology Trends (ITT)_ , Ras Al Khaimah, United Arab Emirates, November 2019.
  * [95] A. Yazdinejad, H. HaddadPajouh, A. Dehghantanha, R. M. Parizi, G. Srivastava, and M.-Y. Chen, “Cryptocurrency malware hunting: A deep recurrent neural network approach,” _Applied Soft Computing_ , vol. 96, p. 106630, 2020.
  * [96] Z. Q. H. X. S. W. J. Batamuliza, “A survey of proxy re-encryption for secure data sharing in cloud computing,” _IEEE Transactions on Services Computing (Early Access Article)_ , pp. 1–1, 2020.
  * [97] H. X. T. Y. H. W. J. F. S. Yu, “A survey of public key encryption with search functionality for cloud-assisted iot,” _IEEE Internet of Things Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [98] B. A. E. D. K. S. T. Martin, “Securing cryptographic keys in the cloud: A survey,” _IEEE Cloud Computing_ , vol. 3, no. 4, pp. 42–56, 2016.
  * [99] K. G. D. G. S. K. P. P. Johri, “A review on cryptography based data security techniques for the cloud computing,” in _Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)_ , Greater Noida, India, March 2021.
  * [100] S. Y. V. G. N. S. E. S. A. Yerukhimovich, “A survey of cryptographic approaches to securing big-data analytics in the cloud,” in _Proceedings of IEEE High Performance Extreme Computing Conference (HPEC)_ , Waltham, MA, USA, September 2014.
  * [101] M. M. D. K. C. S. Yadav, “Secure data transmission in cloud environment using visual cryptography and genetic algorithm: A review,” in _Proceedings of International Conference on Innovations in Control, Communication and Information Systems (ICICCI)_ , Greater Noida, India, August 2017.
  * [102] S. K. A. S. A.-H. F. H. A. Abdullah, “A review of cloud security based on cryptographic mechanisms,” in _Proceedings of International Symposium on Biometrics and Security Technologies (ISBAST)_ , Kuala Lumpur, Malaysia, August 2014.
  * [103] A. Y. A. W. R. S. I. M. Hanafy, “Data security in cloud computing using steganography: A review,” in _Proceedings of International Conference on Innovative Trends in Computer Engineering (ITCE)_ , Aswan, Egypt, 2019.
  * [104] A. Yazdinejad, B. Zolfaghari, A. Azmoodeh, A. Dehghantanha, H. Karimipour, E. Fraser, A. G. Green, C. Russell, and E. Duncan, “A review on security of smart farming and precision agriculture: Security aspects, attacks, threats and countermeasures,” _Applied Sciences_ , vol. 11, no. 16, p. 7518, 2021\.
  * [105] G. A. T. S. M. S. Bhanu, “A survey on secured data sharing using ciphertext policy attribute based encryption in cloud,” in _Proceedings of 8th International Conference on Smart Computing and Communications (ICSCC)_ , Kochi, Kerala, India, July 2021.
  * [106] B. A. M. H. A. A. W. A. H. A. M. Ayaz, “A systematic literature review on cloud computing security: Threats and mitigation strategies,” _IEEE Access_ , vol. 9, pp. 57 792–57 807, 2021.
  * [107] A. S. K. P. G. Shidaganti, “Cloud storage security risks, practices and measures: A review,” in _Proceedings of IEEE International Conference for Innovation in Technology (INOCON)_ , Bangluru, India, November 2020.
  * [108] A. M. A. Joseph, “Blockchain for cloud storage security: A review,” in _Proceedings of 4th International Conference on Intelligent Computing and Control Systems (ICICCS)_ , Madurai, India, May 2020.
  * [109] A. P. N. S. D. R. A. Nayak, “A detailed review of cloud security: Issues, threats & attacks,” in _Proceedings of 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)_ , Coimbatore, India, November 2020.
  * [110] A. M. S. J. J. Kizhakkethottam, “A review on cloud security threats and solutions,” in _Proceedings of International Conference on Soft-Computing and Networks Security (ICSNS)_ , Coimbatore, India, 2015.
  * [111] M. I. M. U. Y. Z. S. Fong, “A critical review of security threats in cloud computing,” in _Proceedings of 3rd International Symposium on Computational and Business Intelligence (ISCBI)_ , Bali, Indonesia, December 2015\.
  * [112] R. A. N. M. A. Almaiah, “Cyber security threats in cloud: Literature review,” in _Proceedings of International Conference on Information Technology (ICIT)_ , Amman, Jordan, July 2021.
  * [113] T. K. D. C. Balakrishna, “Cloud security risk management: A critical review,” in _Proceedings of 9th International Conference on Next Generation Mobile Applications, Services and Technologies_ , Cambridge, UK, September 2015\.
  * [114] S. G. K. D. P. Theng, “A review on intrusion detection techniques for cloud computing and security challenges,” in _Proceedings of 2nd International Conference on Electronics and Communication Systems (ICECS)_ , Coimbatore, India, 2015.
  * [115] K. S. G. A. Sharma, “Idps based framework for security in green cloud computing and comprehensive review on existing frameworks and security issues,” in _Proceedings of International Conference on Computing, Communication and Security (ICCCS)_ , Pointe aux Piments, Mauritius, December 2015\.
  * [116] S. V. B. D. P. Theng, “A survey for secure communication of cloud third party authenticator,” in _Proceedings of 2nd International Conference on Electronics and Communication Systems (ICECS)_ , Coimbatore, India, 2015.
  * [117] K. A. T. G. S. Malande, “Survey paper on a secure and authorized de-duplication scheme using hybrid cloud approach for multimedia data,” in _Proceedings of International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)_ , Chennai, India, August 2017.
  * [118] B. L. A. A. M. Awad, “Towards secure provenance in the cloud: A survey,” in _Proceedings of IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)_ , Limassol, Cyprus, December 2018.
  * [119] T. K. B. C. D. Guruprakash, “A systematic review of the third party auditing in cloud security: Security analysis, computation overhead and performance evaluation,” in _Proceedings of 3rd International Conference on Computing Methodologies and Communication (ICCMC)_ , Erode, India, March 2019.
  * [120] K. S. S. Joseph, “Review on securing medical big data in healthcare cloud,” in _Proceedings of 5th International Conference on Advanced Computing & Communication Systems (ICACCS)_, Coimbatore, India, March 2019.
  * [121] F. R. D. K. A. E. Y. F. A. Z. N. A. A. Wibowo, “Big data security approach in cloud: Review,” in _Proceedings of International Conference on Information Management and Technology (ICIMTech)_ , Jakarta, Indonesia, September 2018.
  * [122] A. Yazdinejad, G. Srivastava, R. M. Parizi, A. Dehghantanha, H. Karimipour, and S. R. Karizno, “Slpow: Secure and low latency proof of work protocol for blockchain in green iot networks,” in _2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)_. IEEE, 2020, pp. 1–5.
  * [123] P. M. S. K. U. G. E. S. P. R. C. Joshi, “Security perspectives of various iot cloud platforms: A review & case study,” in _Proceedings of International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)_ , Greater Noida, India, 2021.
  * [124] N. K. A. R. V. K. H. Walia, “Improve cloud based iot architecture layer security - a literature review,” in _Proceedings of International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)_ , Greater Noida, India, 2021.
  * [125] N. A. A. M. A. S. D. F. A. A. N. Moussa, “The security issues in iot - cloud: A review,” in _Proceedings of 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)_, Langkawi, Malaysia, February 2020\.
  * [126] A. S. H. A. K. Saleem, “Cloud-assisted iot-based scada systems security: A review of the state of the art and future challenges,” _IEEE Access_ , vol. 4, pp. 1375–1384, 2016.
  * [127] E. Rabieinejad, A. Yazdinejad, A. Dehghantanha, R. M. Parizi, and G. Srivastava, “Secure ai and blockchain-enabled framework in smart vehicular networks,” in _2021 IEEE Globecom Workshops (GC Wkshps)_. IEEE, 2021, pp. 1–6.
  * [128] S. M. C. Ramakrishnan, “Review of face recognition techniques for secured cloud data surveillance using machine learning,” in _Proceedings of 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)_ , Sydney, Australia, November 2020.
  * [129] M. W. N. Kratzke, “Towards distributed clouds: A review about the evolution of centralized cloud computing, distributed ledger technologies, and a foresight on unifying opportunities and security implications,” in _Proceedings of International Conference on High Performance Computing & Simulation (HPCS)_, Orleans, France, July 2018.
  * [130] D. S. M. Kumar, “Secure data communication in client-cloud environment: A survey,” in _Proceedings of 7th International Conference on Communication Systems and Network Technologies (CSNT)_ , Nagpur, India, November 2017.
  * [131] A. B. N. M. A. T. Q. N. H. A. F. M. Dakalbab, “Machine learning for cloud security: A systematic review,” _IEEE Access_ , vol. 9, pp. 20 717–20 735, 2021.
  * [132] R. M. Parizi, S. Homayoun, A. Yazdinejad, A. Dehghantanha, and K.-K. R. Choo, “Integrating privacy enhancing techniques into blockchains using sidechains,” in _2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)_. IEEE, 2019, pp. 1–4.
  * [133] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, G. Srivastava, S. Mohan, and A. M. Rababah, “Cost optimization of secure routing with untrusted devices in software defined networking,” _Journal of Parallel and distributed Computing_ , vol. 143, pp. 36–46, 2020.
  * [134] M. Kazemi and A. Yazdinejad, “Towards automated benchmark support for multi-blockchain interoperability-facilitating platforms,” _arXiv preprint arXiv:2103.03866_ , 2021.
  * [135] A. Yazdinejad, A. Bohlooli, and K. Jamshidi, “Performance improvement and hardware implementation of open flow switch using fpga,” in _2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)_. IEEE, 2019, pp. 515–520.
  * [136] A. K. Das, M. Wazid, N. Kumar, A. V. Vasilakos, and J. J. P. C. Rodrigues, “Biometrics-based privacy-preserving user authentication scheme for cloud-based industrial internet of things deployment,” _IEEE Internet of Things Journal_ , vol. 5, no. 6, pp. 4900–4913, 2018.
  * [137] Y. Hailemariam, A. Yazdinejad, R. M. Parizi, G. Srivastava, and A. Dehghantanha, “An empirical evaluation of ai deep explainable tools,” in _2020 IEEE Globecom Workshops (GC Wkshps_. IEEE, 2020, pp. 1–6.
  * [138] J. Sun, D. Chen, N. Zhang, G. Xu, M. Tang, X. Nie, and M. Cao, “A privacy-aware and traceable fine-grained data delivery system in cloud-assisted healthcare iiot,” _IEEE Internet of Things Journal_ , vol. 12, no. 8, pp. 10 034–10 046, 2021.
  * [139] Y. Zhao, L. T. Yang, and J. Sun, “Privacy-preserving tensor-based multiple clusterings on cloud for industrial iot,” _IEEE Transactions on Industrial Informatics_ , vol. 15, no. 4, pp. 2372–2381, 2019.
  * [140] L. Zhang, X. Meng, K.-K. R. Choo, Y. Zhang, and F. Dai, “Privacy-preserving cloud establishment and data dissemination scheme for vehicular cloud,” _IEEE Transactions on Dependable and Secure Computing_ , vol. 17, no. 3, pp. 634–647, 2020.
  * [141] Q. Jiang, N. Zhang, J. Ni, J. Ma, X. Ma, and K.-K. R. Choo, “Unified biometric privacy preserving three-factor authentication and key agreement for cloud-assisted autonomous vehicles,” _IEEE Transactions on Vehicular Technology_ , vol. 69, no. 9, pp. 9390–9401, 2020.
  * [142] A. Secure and P.-P. I. in Vehicular Cloud Advertisement Dissemination, “Qinglei kong and rongxing lu and hui zhu and maode ma,” _IEEE Access_ , vol. 6, pp. 25 040–25 050, 2018.
  * [143] J. Zhou, X. Dong, Z. Cao, and A. V. Vasilakos, “Secure and privacy preserving protocol for cloud-based vehicular dtns,” _IEEE Transactions on Information Forensics and Security_ , vol. 10, no. 6, pp. 1299–1314, 2015.
  * [144] Y. Zheng, R. Lu, S. Zhang, Y. Guan, J. Shao, and H. Zhu, “Toward privacy-preserving healthcare monitoring based on time-series activities over cloud,” _IEEE Internet of Things Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [145] S. Sarkar, S. Chatterjee, S. Misra, and R. Kudupudi, “Privacy-aware blind cloud framework for advanced healthcare,” _IEEE Communications Letters_ , vol. 21, no. 11, pp. 2492–2495, 2017.
  * [146] C. Xu, N. Wang, L. Zhu, K. Sharif, and C. Zhang, “Achieving searchable and privacy-preserving data sharing for cloud-assisted e-healthcare system,” _IEEE Internet of Things Journal_ , vol. 6, no. 5, pp. 8345–8356, 2019.
  * [147] J. Zhang, S. Hu, and Z. L. Jiang, “Privacy-preserving similarity computation in cloud-based mobile social networks,” _IEEE Access_ , vol. 8, pp. 111 889–111 898, 2020.
  * [148] H. Deng, Z. Qin, L. Sha, and H. Yin, “A flexible privacy-preserving data sharing scheme in cloud-assisted iot,” _IEEE Internet of Things Journal_ , vol. 7, no. 12, pp. 11 601–11 611, 2020.
  * [149] H. Xiong, Q. Mei, Y. Zhao, L. Peng, and H. Zhang, “Scalable and forward secure network attestation with privacy-preserving in cloud-assisted internet of things,” _IEEE Sensors Journal_ , vol. 18, no. 19, pp. 8317–8331, 2019.
  * [150] Y.-T. Lee, W.-H. Hsiao, Y.-S. Lin, and S.-C. T. Chou, “Privacy-preserving data analytics in cloud-based smart home with community hierarchy,” _IEEE Transactions on Consumer Electronics_ , vol. 63, no. 2, pp. 200–207, 2017.
  * [151] Y. Sun, Q. Liu, X. Chen, and X. Du, “An adaptive authenticated data structure with privacy-preserving for big data stream in cloud,” _IEEE Transactions on Information Forensics and Security_ , vol. 15, no. 1, pp. 3295–3310, 2020.
  * [152] W. Dou, X. Zhang, J. Liu, and J. Chen, “Hiresome-ii: Towards privacy-aware cross-cloud service composition for big data applications,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 26, no. 2, pp. 455–466, 2015.
  * [153] A. C. Zhou, Y. Xiao, Y. Gong, B. He, J. Zhai, and R. Mao, “Privacy regulation aware process mapping in geo-distributed cloud data centers,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 30, no. 8, pp. 1872–1888, 2019.
  * [154] W. Du, A. Li, Q. Li, and P. Zhou, “Privacy-preserving and secure cloud computing: A case of large-scale nonlinear programming,” _IEEE Transactions on Cloud Computing (Early Access Article)_ , pp. 1–1, 2021.
  * [155] E. Rabieinejad, A. Yazdinejad, T. Hasani, and M. Hammoudeh, “Blockchain-based e-commerce for the covid-19 economic crisis,” in _The 5th International Conference on Future Networks & Distributed Systems_, 2021, pp. 105–112.
  * [156] C. Yang, L. Tan, N. Shi, B. Xu, Y. Cao, and K. Yu, “Authprivacychain: A blockchain-based access control framework with privacy protection in cloud,” _IEEE Access_ , vol. 8, pp. 70 604–70 615, 2020.
  * [157] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, and K.-K. R. Choo, “P4-to-blockchain: A secure blockchain-enabled packet parser for software defined networking,” _Computers & Security_, vol. 88, p. 101629, 2020.
  * [158] B. Chen, L. Wu, H. Wang, L. Zhou, and D. He, “A blockchain-based searchable public-key encryption with forward and backward privacy for cloud-assisted vehicular social networks,” _IEEE Transactions on Vehicular Technology_ , vol. 69, no. 6, pp. 5813–5825, 2020.
  * [159] Y. Wang, A. Zhang, P. Zhang, and H. Wang, “Cloud-assisted ehr sharing with security and privacy preservation via consortium blockchain,” _IEEE Access_ , vol. 7, pp. 136 704–136 719, 2019.
  * [160] Y. Zhang, B. Li, B. Liu, Y. Hu, and H. Zheng, “A privacy-aware pufs-based multiserver authentication protocol in cloud-edge iot systems using blockchain,” _IEEE Internet of Things Journal_ , vol. 8, no. 18, pp. 13 958–13 974, 2021.
  * [161] A. Yazdinejad, A. Bohlooli, and K. Jamshidi, “Efficient design and hardware implementation of the openflow v1. 3 switch on the virtex-6 fpga ml605,” _The Journal of Supercomputing_ , vol. 74, no. 3, pp. 1299–1320, 2018.
  * [162] Q. Jiang, J. Ma, and F. Wei, “On the security of a privacy-aware authentication scheme for distributed mobile cloud computing services,” _IEEE Systems Journal_ , vol. 12, no. 2, pp. 2039–2042, 2018.
  * [163] Y. Tong, J. Sun, S. S. M. Chow, and P. Li, “Cloud-assisted mobile-access of health data with privacy and auditability,” _IEEE Journal of Biomedical and Health Informatics_ , vol. 18, no. 2, pp. 419–429, 2014.
  * [164] M. Li, N. Ruan, Q. Qian, H. Zhu, X. Liang, and L. Yu, “Spfm: Scalable and privacy-preserving friend matching in mobile cloud,” _IEEE Internet of Things Journal_ , vol. 4, no. 2, pp. 419–429, 2017.
  * [165] D. He, N. Kumar, M. K. Khan, L. Wang, and J. Shen, “Efficient privacy-aware authentication scheme for mobile cloud computing services,” _IEEE Systems Journal_ , vol. 12, no. 2, pp. 1621–1631, 2018.
  * [166] X. Liu, W. Ma, and H. Cao, “Mbpa: A medibchain-based privacy-preserving mutual authentication in tmis for mobile medical cloud architecture,” _IEEE Access_ , vol. 7, pp. 149 282–149 298, 2019.
  * [167] T. He, E. N. Ciftcioglu, S. Wang, and K. S. Chan, “Location privacy in mobile edge clouds: A chaff-based approach,” _IEEE Journal on Selected Areas in Communications_ , vol. 11, no. 35, pp. 2625–2636, 2017.
  * [168] T. Li, Z. Qiu, L. Cao, D. Cheng, W. Wang, X. Shi, and Y. Wang, “Privacy-preserving participant grouping for mobile social sensing over edge clouds,” _IEEE Transactions on Network Science and Engineering_ , vol. 8, no. 2, pp. 865–880, 2021.
  * [169] T. Wang, J. Zeng, M. Z. A. Bhuiyan, H. Tian, Y. Cai, Y. Chen, and B. Zhong, “Trajectory privacy preservation based on a fog structure for cloud location services,” _IEEE Access_ , vol. 5, pp. 7692–7701, 2017.
  * [170] H. A. A. Hamid, S. M. M. Rahman, M. S. Hossain, A. Almogren, and A. Alamri, “A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography,” _IEEE Access_ , vol. 5, pp. 22 313–22 328, 2017.
  * [171] Q. Kong, R. Lu, F. Yin, and S. Cui, “Privacy-preserving continuous data collection for predictive maintenance in vehicular fog-cloud,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 22, no. 8, pp. 5060–5070, 2021.
  * [172] G. Han, X. Miao, H. Wang, M. Guizani, and W. Zhang, “Cpslp: A cloud-based scheme for protecting source location privacy in wireless sensor networks using multi-sinks,” _IEEE Transactions on Vehicular Technology_ , vol. 68, no. 3, pp. 2739–2750, 2019.
  * [173] N. Wang, J. Fu, J. Li, and B. K. Bhargava, “Source-location privacy protection based on anonymity cloud in wireless sensor networks,” _IEEE Transactions on Information Forensics and Security_ , vol. 15, no. 1, pp. 100–114, 2020.
  * [174] A. Yazdinejad, R. M. Parizi, G. Srivastava, A. Dehghantanha, and K.-K. R. Choo, “Energy efficient decentralized authentication in internet of underwater things using blockchain,” in _2019 IEEE Globecom Workshops (GC Wkshps)_. IEEE, 2019, pp. 1–6.
  * [175] M. Oqaily, Y. Jarraya, M. Mohammady, S. Majumdar, M. Pourzandi, L. Wang, and M. Debbabi, “Segguard: Segmentation-based anonymization of network data in clouds for privacy-preserving security auditing,” _IEEE Transactions on Dependable and Secure Computing_ , vol. 18, no. 5, pp. 2486–2505, 2021.