# A Holistic View on Data Protection for Sharing, Communicating, and Computing
Environments: Taxonomy and Future Directions

Ishu Gupta*
Cloud Computing Research Center
Department of Computer Science and Engineering
National Sun Yat-sen University
Kaohsiung, Taiwan
<EMAIL_ADDRESS>
Ashutosh Kumar Singh
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
136119
<EMAIL_ADDRESS>

###### Abstract

The data is an important asset of an organization and it is essential to keep
this asset secure. It requires security in whatever state is it i.e. data at
rest, data in use, and data in transit. There is a need to pay more attention
to it when the third party is included i.e. when the data is stored in the
cloud then it requires more security. Since confidential data can reside on a
variety of computing devices (physical servers, virtual servers, databases,
file servers, PCs, point-of-sale devices, flash drives, and mobile devices)
and move through a variety of network access points (wireline, wireless, VPNs,
etc.), there is a need of solutions or mechanism that can tackle the problem
of data loss, data recovery and data leaks. In this context, the paper
presents a holistic view of data protection for sharing and communicating
environments for any type of organization. A taxonomy of data leakage
protection systems and major challenges faced while protecting confidential
data are discussed. Data protection solutions, Data Leakage Protection
System’s analysis techniques, and, a thorough analysis of existing state-of-
the-art contributions empowering machine learning-based approaches are
entailed. Finally, the paper explores and concludes various critical emerging
challenges and future research directions concerning data protection.

_Keywords_ Cloud Computing $\cdot$ Data Protection $\cdot$ Leakage Detection
$\cdot$ Leakage Prevention $\cdot$ Information Security

##  1 Introduction

Data is recognized as the most vital asset of an enterprise because it defines
each organization’s uniqueness [1]. In any organization, data is the main
foundation of information, knowledge and ultimately the wisdom for correct
decisions and actions [2, 3]. It might be helping to cure a disease, boost a
company’s revenue, make a building more efficient or be responsible for those
targeted as we keep seeing [4]. If the data is relevant, complete, accurate,
timely, consistent, meaningful, and usable, then it will surely help in the
growth of the organization [5, 6]. Companies that do not understand the
importance of data management are less likely to survive in the modern
economy. Therefore, it is essential to understand the importance of data
management in companies [7, 8]. Data should be carefully cultivated, managed,
and refined into information that will allow an enterprise to better serve
their community and ensure to remain viable in today’s competitive landscape
[9, 10].

In the real world scenario, a data distributor has to share the sensitive data
of an entity among various stakeholders such as business partners, customers,
and employees within or outside the organization’s premises for doing business
[11, 12]. But the receiving entity may misuse this data and can leak it
deliberately or inadvertently to some unauthorized third party [13, 14]. Data
leakage is the intentional or unintentional distribution of confidential or
private information or data to an unauthorized malicious entity [15, 16].
Critical data in various organizations include patient information,
Intellectual Property (IP), personal information like credit card data,
financial information and various other information depending upon the
organization [17, 18].

Data leakage can cause a serious threat to the organization’s confidentiality
as the number of incidents as well as the cost procured due to these leakages
to continue to increase [19]. It poses a great challenge for any organization
[20, 21]. It can destruct the enterprise’s reputation and goodwill, diminish
shareholder’s value and decline the firm’s status and rank [22, 23]. Mostly
precarious threats that any company faces, are because of insiders in the
company, after all, as the insiders know the company’s internal system. To
handle the attacks caused by insiders of the company is a most difficult task
[24, 25].

It is essential to protect the confidential information as it increases the
risk of falling the sensitive information in unauthorized hands and then it
can be misused by unauthorized third party [26, 27]. Thus, it has become
consequential for any organization to detect and prevent such leakage.
Conversely, limiting the sharing of data in order to preserve the security and
privacy of confidential information might reduce the organization’s
performance. It influences the potential to perform the operation that can
serve best to the organization and its customers [28]. Data leakage detection
and prevention significantly play an inevitable role ineffective protection of
the data. In this paper, concepts, challenges, existing solutions, research
gaps, and future directions for sharing, communicating, and computing
environments for any type of organization are discussed in detail.

Organization: Section 2 discusses the motivation for carrying out this study.
Section 3 entails the challenges faced by the data protection system while
protecting the data followed by the categorization of data protection
solutions in section 4. Section 5 describes the concept of data leakage
protection systems (DLPTSs) including data states, deployment schema, and
DLPTSs analysis technique. An analysis and comparison of existing approaches
and solutions along with their strengths and weaknesses are described
comprehensively in section 6. Thereafter, the emerging challenges and future
research direction in the field are entailed in section 7 followed by the
conclusion and future scope of the work in section 8.

##  2 Motivation

In recent years, the internet and related technologies have grown rapidly [29,
30]. It offered the unrivaled capability to access and redistribute the
digital data [31, 32]. It is easy to copy a huge amount of digital data at
almost no cost that can be transferred in a very short time via the internet
[33, 34]. The various organizations use this facility to enhance their
capability by transferring data from one place to another but it involves a
number of threats in transferring the confidential data as it can be made
public by any malicious entity [35].

According to an exotic chronology of data breaches explored by Privacy Rights
Clearinghouse (PRC), $90,61,64,759$ records have been breached in the united
state alone from $5,278$ data breaches made public since $2005$ [36]. It is
not hard to speculate that it is just a fingertip of an iceberg as most of the
time, the data leakage cases are not recorded due to concern of restrictive
penalties and loss of customer trust. In the year $2016$, $4,004,004$ records
have been breached in the health care, medical providers, and medical
insurance services organization alone and $300$ breaches made public [37].

According to the annual cost of data breach study conducted by Ponemon
Institute, the average consolidated cost of a data breach is $\$4$ million.
This study states that the cost procured for each stolen or lost record
consisting of confidential and sensitive information increased from a
consolidated average of $\$154$ to $\$158$ [38]. In highly regulated
industries like healthcare, data breach cost is $\$355$ per record which is
$\$100$ more than in $2013$ [39]. Because of these reasons; the data leakage
problem is increasing day by day.

A study reports that the number of leaked confidential data records has become
$10$ times within $4$ years and it reached up to $1:1$ billion in $2014$ and
it is kept on increasing as the number of users are increasing, also the
malicious entity [40]. Fig. 1 [41] presents the development of cyberattacks
over time. It presents the recorded number of data breaches and records
exposed in the United States between $2005$ and $2020$. In $2020$, the number
of data breaches in the United States amounted to $1001$ with $155.8$ million
records exposed which is much higher compared to the past years.

Figure 1: Annual number of data breaches and exposed records in the United
States from 2005 to 2020 (in millions).

The data leakage problem has reached a new dimension. There is a need for a
general mechanism that can handle the data leakage problem by preventing the
data leakage and by identifying the malicious entity responsible for data
leakage. As the data is shared among multiple entities thus it becomes
difficult to identify the malicious entity who has leaked the confidential
information [42, 43]. In this paper, comprehensive solutions are explored that
solve the data leakage problem by preventing the leakage and identifying the
malicious entity who has leaked the organizational sensitive data.

##  3 Challenges in Data Protection

Like any other security mechanism, data protection systems also face many
challenges when protecting confidential data. There are mainly seven
challenges identified for data protection. To implement a successful data
protection system, these challenges must be considered and addressed
adequately [44, 45].

###  3.1 Leaking Channels

For Data-At-Rest and Data-In-Use, confidential data can be leaked through
channels such as USB ports, CD drives, web services, and printed documents.
Further, leaking channels associated with Data-In-Motion, such as web services
and file sharing, might be extremely challenging, since these channels may be
business prerequisites. To ensure maximum security for data passing through
these channels, extensive traffic filtering must be carried out.

###  3.2 Human Factor

It is always difficult to predict human behavior because it is influenced by
many psychological and social factors. Many human actions are affected by
subjectivity in making decisions, such as defining the secrecy level of data,
assigning access rights to specific users.

###  3.3 Access Right

It is important for DLPSs to be able to distinguish between different users
based on their privileges and permissions. Without a proper definition of
access rights, DLPSs cannot decide whether or not the data is being accessed
by a legitimate user, and then it can be misused by a malicious entity.

###  3.4 Encryption and Steganography

Network-based DLPSs and DLDSs attempt to identify copies of confidential data
using various analysis techniques, and then compare them to the original data.
However, the use of strong encryption algorithms makes it very difficult or
nearly impossible to analyze the data content. Furthermore, the steganography
technique can be used to maliciously leak confidential data since it is highly
likely to bypass traffic inspection mechanisms.

###  3.5 Data Modification

Some DLPSs and DLDSs use data hashing to check outgoing traffic and to monitor
the outgoing data respectively. The problem with hashing is that any
modification to the original document can lead to a totally different hash
value, resulting in disclosure. Furthermore, some DLDSs use watermarking and
steganography to hide the secret data/information that can be modified by some
malicious entity.

###  3.6 Scalability and Integration

Like many other security mechanisms, data protection systems too can be
affected by the amount of data being processed. DLPSs tend to have a poor
integration/association with other security mechanisms in a network. This is
because some of their mandatory features already exist in other solutions such
as firewalls, IDSs, and proxy servers which may arise inconsistencies.

###  3.7 Data Classification

If the data is not classified into different levels, data protection systems
will not be able to distinguish between confidential and normal data/traffic.
Hence, without proper data classification, confidential data can easily be
revealed even in the presence of data protection systems.

##  4 Data Protection Solutions

Data leakage prevention and data leakage detection solution are provided to
protect the data as shown in Fig. 2.

Figure 2: Solution for data protection.

###  4.1 Data Leakage Prevention

Data leakage/loss prevention (DLP) is typically defined as any solution or
process that identifies confidential data, tracks that data as it moves
through and out of the enterprise, and prevents unauthorized disclosure of
data by creating and enforcing disclosure policies [46, 47].

DLPSs are defined as designated analytical systems used to protect data from
unauthorized disclosure at all states using remedial actions triggered by a
set of rules. This definition contains three main attributes that distinguish
DLPSs from conventional security measures. First, DLPSs have the ability to
analyze the content of confidential data and the surrounding context. Second,
DLPS scans be deployed to provide protection of confidential data in different
states, that is, in transit, in use, and at rest. The third attribute is the
ability to protect data through various remedial actions, such as notifying,
auditing, blocking, encrypting, and quarantining. The protection normally
starts with the ability to detect potential leaks through heuristics, rules,
patterns, and fingerprints. The prevention then happens accordingly [48, 49].

###  4.2 Data Leakage Detection

Data leakage detection (DLD) is typically defined as any solution or process
that identifies the unauthorized disclosure of confidential data. Data leakage
detection systems (DLDSs) are specially designed systems that have the ability
to monitor and protect confidential data, detect misuse of confidential data
and identify the malicious entity responsible for data leakage [50, 51].

##  5 Data Leakage Protection System

A taxonomy of Data Leakage Protection System (DLPTS) is represented in Fig. 3
that represents, what data to be secured, deployment schemes, solution for
data protection and the remedial action to be taken.

Figure 3: A taxonomy of Data leakage protection system (DLPTS).

###  5.1 Data State

Data states include “Data-At-Rest (DAR)", “Data-In-Use (DIU)" and “Data-In-
Motion (DIM)". Data-At-Rest is the type of data that is stored in repositories
either locally or at remote locations. It consists of application databases,
backup files, and file systems. It is normally protected by strong access
controls, including physical and logical mechanisms. Data-In-Use is the data
that is accessible to the user in the forms of documents, emails, and
applications. This type of data appears in plain text, so it can be easily
interpreted and processed. Data-In-Motion is the data being transmitted from
one node to another. This type of data travels internally between nodes within
the same network or externally between nodes that belong to different networks
[52].

###  5.2 Deployment Schema

DLPS schemes are deployed at the endpoints to protect the data at rest and
data in use and in the network to protect the data in motion/transit.
Depending on the targeted data for protection, DLPS deployment can take many
forms. For example, DLPSs that deal with Data-At-Rest are normally focused on
protecting known data. The protection comes in the forms of preventing access
to data based on predefined security policies. Also, this type of DLPS helps
in protecting data at rest by encrypting entire file systems. Furthermore, It
scans, identifies, and secures confidential data in data repositories while
auditing and reporting security policy violations [53].

Protecting Data-In-Use requires built-in software that acts like a DLP agent
on endpoints. This agent is responsible for disabling or enabling access to
applications that deal with confidential data. It is also responsible for
blocking confidential data transfer through portable media, that is, CDs, USB
drives, and memory cards. Furthermore, it restricts copying, pasting, and
editing of confidential data as well as restricting making hard copies through
printers. It audits all activities related to confidential data access [54].

For Data-In-Motion, DLP appliances are normally used. They come with special
processing capabilities to handle large amounts of data. This type of DLPS is
responsible for inspecting all outbound traffic for confidential data. It also
acts like a proxy server when accessing some applications with confidential
data. Moreover, it proactively reports and alerts security administrators and
users about potential data leaks. Finally, it coordinates with other security
mechanisms such as Secure Sockets Layer (SSL) proxies and network firewalls
[55].

###  5.3 DLPTSs Analysis Technique

Context-based DLPTSs focus on the metadata (context), such as size, timing,
source, and destination surrounding confidential data, rather than the
sensitivity of the content to detect any potential leaks. Content-based
analysis DLPSs are more common than and preferable to those that are context-
based since it is more logical to focus on the protection of the data itself
than on the surrounding context [55].

A typical content-based DLPTS works by monitoring sensitive data in its
repository or on the go, mainly by using regular expressions, data
fingerprinting, and statistical analysis. Regular expressions are normally
used under a certain rule such as detecting social security numbers and credit
card numbers. The problem with DLPTSs using regular expressions analysis is
that they offer limited data protection and have high false-positive rates
[55].

DLPTSs using data fingerprints have better coverage for sensitive data because
they have the ability to detect and prevent the leakage of a whole document or
parts of a document. However, traditional fingerprinting can lose track when
the sensitive data is altered or modified [56, 57].

Although not widely used in DLPTSs, statistical analysis can work in a fuzzy
environment where the sensitive data is not well structured. The main
advantage of such a technique is the ability to identify sensitive documents
even after extreme modification. In particular, DLPTSs with statistical
analysis capabilities can use machine learning algorithms or Bayesian
probability to identify altered documents. They can also use text-clustering
techniques to construct scattered traces of sensitive data. N-gram analysis
and term weighting analysis are the main statistical analysis techniques.
N-gram statistical analysis is the way to analyze data based on the frequency
of data of interest. It is a method based on machine learning that classifies
enterprise documents as sensitive or not [54]. Term weighting is a statistical
method that indicates the importance of a word within a document. It is
normally used in text classification.

A third technique called content tagging is used in some DLPTSs. This
technique is used to tag the file containing confidential data. It can
preserve the identity of the file but not the contained confidential data.

##  6 Current Approaches of DLPT

On the basis of the review, there are mainly seven approaches for data
protection as represented in Fig. 4 that are summarized in Table 1 with their
strengths and weaknesses.

Figure 4: Classification of DLPTSs. Table 1: Strength and weakness of current DLPT approaches. Approach |  Strengths |  Weaknesses
---|---|---
Policy and access rights |  • suitable for any organization if access rights and data classification are properly established • easy to manage • suitable for Data-At-Rest and Data-In-Use • strong prevention mechanism |  • affected by improper data classification • affected by the access control policy in use • not a detective method, hence if a leak is happening the method is ineffective
Virtualization and isolation |  • requires small hardware implementation • dynamic as it does not need regular administrative interference • accessing sensitive data can use existing data classification |  • not mature enough • produces considerable amount of overheads • not a detection method
Cryptographic approaches |  • strong cryptography can produce maximum security • cryptographic methods are wieldy to use and have many options |  • cryptography can secure sensitive data but may not deny its existence • does not detect data leak • confidential data can be accessed by weak credentials
Quantifying and limiting |  • goes beyond studying sensitive data, and focus on the leaking channels • useful against specific types of attacks such as salami attacks • effective for all data states |  • does not ensure total blockage of the leaking channel • limited to specific situations or scenarios • can disrupt workflow
Social and behavior analysis |  • proactive data leakage prevention by detecting malicious relations • suitable for all data states |  • produces a high level of false-positive requires regular administrative interference • requires a huge amount of profiling and indexing
Data identification |  • very strong in detecting unmodified data • very low false positive level for analysis using fingerprints • some robust hashing can detect modified data |  • extremely modified data cannot be detected • lacking semantic understanding
Data mining and text clustering |  • can predict future data leaks powerful in detecting unstructured data • less dependent on administrative help flexible and adaptable |  • requires a great deal of processing requires learning phase, which means many false positives

The models that handle with data leakage problem by embedding the code in the
document are presented in [58, 59, 60]. Backes et al. provide a generic data
lineage framework called LIME for data flow across multiple entities having
two principal roles i.e. owner and consumer. The guilty entity is identified
by developing a data transfer protocol between two entities via considering an
oblivious transfer, watermarking, and signature primitives. This method
considers the possible data leakage and the corresponding constraints at the
design stage. A model that identifies the guilty party who is responsible for
leaking critical organizational data is given by Kumar et al. This method is
able to find the exact client who leaked the data but it is unable to detect
the guilty client if embedded code is modified or completely destroyed by the
client.

The model introduced in [61, 62] helps to identify the guilty agent who leaks
the data provided by the distributor. In [61] the model assesses the guilt of
various agents. It consists of various algorithms for the distribution of data
among multiple agents which improve the chances of identifying a leaker and
then the fake objects are added to the distributed set. Data is allocated to
various agents in such a manner that there will be minimum overlapping of data
among various agents. Later on, if any agent leaked his confidential data at
some unauthorized place then the guilty party can be found on the basis of the
data allocated to the agents. Kumar et al. introduce the allocation strategies
that work on the basis of No- wait model and increase the chances of
identifying the guilty party. These methods have the benefit that the agent
can not destroy the information after leaking the data as in the case of
watermarking but it cannot identify the exact data leaker in the case when the
same data is allocated to multiple agents or overlapping of data among agents
increases.

The method introduced in [63] enables the data owner to depute the detection
operation safely to a semi-host provider without exposing the critical data to
the provider. This method is implemented using the fuzzy fingerprint technique
to elevate data privacy at the time of data leak detection operations. The
method provides the privacy-preserving Data Leakage Detection (DLD) solution
to handle data leaks in situations where a set of sensitive data digests is
utilized for detection.

To protect sensitive information from unauthorized parties, Bishop et al.
present an information leakage detection (ILD) agent system in [64]. It is a
mobile agent-based approach that brutalizes the process of discovering and
coloring perceptive hosts file systems and observing the colored file system
for detection of potential information leakage. A concept presented is based
on assigning a sensitivity score to data sets for mitigating the data misuse
and data leakage incidents in database system [65].

Other solution for data leaks includes sequence alignment techniques [66, 67,
68, 69], classification technique [70], network-based data leak detection
solution [71, 72, 73]. In [67] solution for the detection of transformed data
leaks is presented by detecting complex data leak patterns using sequence
alignment techniques. A new class of health information security intrusions
that exploits physiological information leakage to compromise privacy is
presented in [74]. A map-reduce framework is introduced by Liu et al. for the
detection of exposed sensitive contents in [63]. A network-wide method based
on black-box differencing for restraining and controlling the flow of
confidential data within a network is presented in [75, 76], which determines
when the secret data is being leaked. [77, 78, 79] present the way for data
leakage prevention. In [62], it is specified that solutions for data leakage
preventions are mainly deployed to prevent unintentional leakage incidents.
Table LABEL:table2 summarizes the most relevant work of the field.

Table 2: Summary of most relevant work in the review. Paper and Category |  Method |  Analysis |  Suitable for |  Contribution |  Limitation
---|---|---|---|---|---
Wuchner et al. [80], 2012 (Policy and access rights) |  Detective/ Preventive |  Context |  In use |  UC4Win, a data loss prevention solution for Microsoft Windows operating systems. |  Requires predefined policy. Cannot identify sensitive data.
Squicciarini et al. [81], 2010 (Policy and access rights) |  Preventive |  Context |  In use |  Introduces a three layers data protection framework. |  Requires a pre-defined classification for data. Mis- classified sensitive data can be leaked.
Griffin et al. [82], 2005 (Virtualization and isolation) |  Preventive |  Context |  In use |  Proposes Virtual Trusted Domains (VTD) to offload processes to secure environments. |  Imposes challenge to computational capabilities.
Wu et al. [53], 2011 (Virtualization and isolation/ Cryptographic approaches) |  Preventive |  Context |  At rest/ In use |  Introduces a combination of encrypted storage and virtual environment to prevent data leakage. |  Suitable for data at rest only. Cannot prevent data leakage caused by privileged used.
Blanke [83], 2011 (Cryptographic approaches) |  Preventive |  Context |  At rest |  Uses ephemeral encryption to protect data whenever accessed by a user. |  Requires a pre-implementation of an encrypted file system.
Yoshihama et al. [84], 2010 (Quantifying and limiting) |  Detective |  Content/ Context |  In motion |  Uses an application-level proxy to detect potential data leakage risks. |  Cannot detect data leaked through covert channels.
Borders et al. [85], 2009 (Quantifying and limiting) |  Detective |  Content |  In motion |  Constrains the maximum volume of sensitive data in web traffic. |  Unable to filter parts of URLs that contains random numbers to prevent cashing.
Suen et al. [86], 2013 (Quantifying and limiting/ Social and behavior analysis) |  Detective |  Context |  In motion |  Uses S2 Logger to track files while traveling in the cloud. |  Based on content tagging. Cannot track sensitive content.
Boehmer [87], 2010 (Social and behavior analysis) |  Detective/ Preventive |  Context |  In use |  Uses case-based reasoning (CBR) in combination with directed acyclic graph (DAG) and Hamming similarity function. |  Needs existing or synthetic compliance profiles for comparison process.
Shapira et al. [88], 2013 (Data identification) |  Detective |  Content |  In use |  Robust fingerprinting to overcome shortcoming in ordinary hashing. |  Requires extensive data indexing for both sensitive and normal data.
Shu et al. [71], 2013 (Data identification) |  Detective |  Content |  In motion |  Uses message shingles/fuzzy fingerprints to detect in advertent data leak in network traffic. |  Modified data can cause false negatives because the shingles fingerprints are different from the original ones.
Hart et al. [54], 2011 (Data mining and Text clustering) |  Detective |  Content |  In use |  Uses SVM Machine learning to classify documents to private and public. |  Inflexible, Limited to two categories.
Lindell et al. [89], 2000 (Data mining and Text clustering) |  Preventive |  Content |  At rest/ In use |  Sharing confidential data on the union of the entities databases, without releasing unnecessary information. |  Theoretically proven but lacking practical experiments.
Marecki et al. [90], 2010 (Data mining and Text clustering) |  Detective |  Context |  In motion |  Uses Partially Observed Markov Decision Processes (POMDPs) over decision epochs. |  POMDP requires huge amount of calculations.
Gomez-Hidalgo et al. [91], 2010 (Data mining and Text clustering) |  Detective/ Preventive |  Content |  In motion |  NER (named entity recognition) approach is used to identify and extract words from texts. |  Named entity recognition could be affected by spelling mistakes and connected words.
Sokolova et al. [92], 2009 (Data mining and Text clustering) |  Detective |  Content |  In motion |  Uses support vector machine to classify enterprise documents as sensitive non-sensitive. |  Not fixable because it classifies data to public or private only.
Parekh et al. [93], 2006 (Data mining and Text clustering) |  Detective |  Content |  In motion |  A new approach to enable the sharing information of suspicious payloads. |  Polymorphic/ obfuscated worms and mimicry attacks may create a big challenge.
Carvalho et al. [94], 2009 (Data mining and Text clustering) |  Detective |  Content/ Context |  In motion |  Presents an extension –Cut Once- to “Mozilla Thunderbird". |  Introduces high level of false positives, since it requires existing messages in the sent folder.
Zilberman et al. [95], 2010 (Data mining and Text clustering/ Social and behavior analysis) |  Detective/ Preventive |  Content/ Context |  In motion |  Uses TF-IDF and cosine similarity to compute existing links between users. |  High false positive rates because of short history between senders and recipients.
Carvalho and Cohen [96], 2007 (Data mining & Text clustering/ Social & behavior analysis) |  Detective |  Content/ Context |  In motion |  Predicts unintended message recipients. |  Can create false outliers because of limited interaction history.

##  7 Emerging Challenges and Future Research Directions

Data protection mechanism should make sure that the data is safe enough from
all the internal as well as external threats so that there should not be any
problem such as loss of data or data theft. Many methods and solutions have
been proposed to protect the data from leakage/loss but still there exist some
research gaps in the provided solutions. We need an ideal solution that can
secure, manage confidential data and locate the malicious agent and
activities. The following research gaps are identified based on the literature
review:

  1. 1.

Increased Overhead: When the data is stored at a third party or somewhere else
in the database or repositories, the security mechanism such as encryption
scheme, etc. are applied to the whole data which reduces the utility of data
and increase the overhead of the service provider, Furthermore some of the
approaches require a huge amount of calculations resulting in delay and
increased overhead.

  2. 2.

Static Request handling: Solution has been provided for the detection of a
malicious agent by providing a guilty agent model.

  3. 3.

Single Objective Approaches: The approaches provide the solution either for
detection of guilty agent or prevention of loss of data. These approaches are
suitable for data-at-rest or data-in-use or data-in-motion. Some approaches
provide prevention solutions but cannot identify data as sensitive. For the
better protection of data, there is a need for a hybrid approach that fulfills
all the objectives.

  4. 4.

Involves Data modification: The guilty agent is identified by hiding the
information in the requested document which may involve modification of data.
Furthermore, DLP uses techniques such as regular expression, fingerprinting,
data mining/ text clustering, etc. to identify the sensitive data, but if the
information is too much modified then these techniques cannot extract the
sensitive data.

  5. 5.

High false-positive rate: The approaches used for DLP require a learning phase
that may involve a high false-positive rate.

The following are the research directions in order to fill the identified
research challenges:

  1. 1.

To make a balance between data utility and security and reducing overheads by
proper classification.

  2. 2.

To formulate a Data leakage Detection System that handles the user request in
a dynamic manner.

  3. 3.

Efforts will be made in developing the system with high accuracy.

  4. 4.

Development of a new multi-objective approach that provides both Data Leakage
Detection and Prevention

##  8 Conclusions and Future Scope

Prevention of data disclosure to unauthorized entities is one of the main
goals of information security. According to datalossdb ($2015$) report, in the
year $2014$, about $50\%$ of recorded data leakage occurred in the business
sector, about $20\%$ occurred in the government sector and about $30\%$
occurred in the health and education sectors. Although some reported leaks
were not detrimental to organizations, others have caused several million
dollars worth of damage. Business credibility is compromised when sensitive
data is leaked to competitors. The developments to be carried out in research
work will be helpful in business, governmental, private, etc. sectors with the
following advantages:  (i) Provide Data protection.  (ii) Maintains Privacy
and Security of data.  (iii) Mitigates the risk of data leakage.  (iv) Solves
the controversy raised due to information leakage.

## References

  * [1] I. Gupta and A. K. Singh, “An Integrated Approach for Data Leaker Detection in Cloud Environment,” _Journal of Information Science and Engineering_ , vol. 36, pp. 993–1005, Sep. 2020.
  * [2] A. K. Singh and I. Gupta, “Online Information Leaker Identification Scheme for Secure Data Sharing,” _Multimedia Tools and Applications_ , vol. 79, no. 41, pp. 31 165–31 182, November 2020.
  * [3] Khushbu, P. Nishad, V. Kashyap, and I. Gupta, “A Classification and Distribution Model for Data Leakage Prevention and Detection,” _International Research Journal of Modernization in Engineering Technology and Science_ , vol. 3, no. 2, pp. 348–354, Feb. 2021.
  * [4] I. Gupta and A. K. Singh, “GUIM-SMD: Guilty User Identification Model using Summation Matrix-based Distribution,” _IET Information Security_ , vol. 14, pp. 773–782, November 2020.
  * [5] G. Batra, H. Singh, I. Gupta, and A. K. Singh, “Best Fit Sharing and Power Aware (BFSPA) Algorithm for VM Placement in Cloud Environment,” in _2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall)_. IEEE, 2017, pp. 1–4.
  * [6] I. Gupta and A. K. Singh, “Dynamic Threshold based Information Leaker Identification Scheme,” _Information Processing Letters_ , vol. 147, pp. 69 – 73, 2019.
  * [7] RingLead, “The Importance of Data Management In Companies,” 2021. [Online]. Available: https://www.ringlead.com/blog/the-importance-of-data-management-in-companies/#.WWLh09Tyu00
  * [8] I. Gupta, T. K. Madan, S. Singh, and A. K. Singh, “Stock Market Forecasting using Historical and Sentiment Analysis,” _CoRR_ , 2022.
  * [9] Debbie King, “Association Analytics, Data is an Asset,” February 2013. [Online]. Available: https://associationanalytics.com/blog/data-is-an-asset/
  * [10] I. Gupta, V. Sharma, S. Kaur, and A. K. Singh1, “An Efficient Parkinson’s Disease Prediction Model based on Random Forest Classification,” _CoRR_ , 2022.
  * [11] I. Gupta, R. Gupta, A. K. Singh, and R. Buyya, “MLPAM: A Machine Learning and Probabilistic Analysis Based Model for Preserving Security and Privacy in Cloud Environment,” _IEEE Systems Journal_ , vol. 15, no. 3, pp. 4248–4259, 2021.
  * [12] A. Acharya, H. Prasad, V. Kumar, I. Gupta, and A. K. Singh, “Host Platform Security and Mobile Agent Classification: A Systematic Study,” in _Computer Networks and Inventive Communication Technologies_ , vol. 58. Singapore: Springer Singapore, 2021, pp. 1001–1010, data Engineering and Communications Technologies.
  * [13] I. Gupta and A. K. Singh, “A Framework for Malicious Agent Detection in Cloud Computing Environment,” _International Journal of Advanced Science and Technology (IJAST)_ , vol. 135, pp. 49–62, Feb 2020.
  * [14] P. Godha, S. Jadon, A. Patle, I. Gupta, B. Sharma, and A. Kumar Singh, “Architecture, an Efficient Routing, Applications, and Challenges in Delay Tolerant Network,” in _2019 International Conference on Intelligent Computing and Control Systems (ICCS)_. IEEE, 2019, pp. 824–829.
  * [15] P. Godha, S. Jadon, A. Patle, I. Gupta, B. Sharma, and A. K. Singh, “Flooding and Forwarding Based on Efficient Routing Protocol,” in _International Conference on Innovative Computing and Communications_ , vol. 1166. Singapore: Springer Singapore, 2021, pp. 215–223, advances in Intelligent Systems and Computing.
  * [16] I. Gupta, S. Mittal, A. Tiwari, P. Agarwal, and A. K. Singh, “TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage Prevention Model,” _CoRR_ , 2022.
  * [17] D. Saxena, I. Gupta, J. Kumar, A. K. Singh, and X. Wen, “A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center,” _IEEE Systems Journal_ , pp. 1–12, 2021.
  * [18] I. Gupta and A. K. Singh, “SELI: Statistical Evaluation based Leaker Identification Stochastic Scheme for Secure Data Sharing, journal = IET Communications,” vol. 14, pp. 3607–3618, December 2020.
  * [19] P. Tiwari, S. Mehta, N. Sakhuja, I. Gupta, and A. K. Singh, “Hybrid Method in Identifying the Fraud Detection in the Credit Card,” in _Evolutionary Computing and Mobile Sustainable Networks_ , vol. 53. Singapore: Springer Singapore, 2021, pp. 27–35, data Engineering and Communications Technologies.
  * [20] Khushbu, P. Nishad, V. Kashyap, I. Gupta, and A. K. Singh, “An Organized Study on Data Divulge Elimination and Discernment,” in _Computer Networks and Inventive Communication Technologies_ , vol. 58. Singapore: Springer Singapore, 2021, pp. 569–578, data Engineering and Communications Technologies.
  * [21] U. Arora, S. Verma, I. Gupta, and A. K. Singh, “Implementing Privacy using Modified Tree and Map Technique,” in _2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall)_. IEEE, 2017, pp. 1–5.
  * [22] V. Sharma, S. Jalwa, A. R. Siddiqi, I. Gupta, and A. K. Singh, “A Lightweight Effective Randomized Caesar Cipher Algorithm for Security of Data,” in _Evolutionary Computing and Mobile Sustainable Networks_ , vol. 53. Singapore: Springer Singapore, 2021, pp. 411–419, data Engineering and Communications Technologies.
  * [23] P. Agarwal, S. Mittal, A. Tiwari, I. Gupta, A. K. Singh, and B. Sharma, “Authenticating Cryptography over Network in Data,” in _2019 International Conference on Intelligent Computing and Control Systems (ICCS)_. IEEE, 2019, pp. 632–636.
  * [24] A. Kesharwani, A. Nag, A. Tiwari, I. Gupta, B. Sharma, and A. K. Singh, “Real-Time Human Locator and Advance Home Security Appliances,” in _Evolutionary Computing and Mobile Sustainable Networks_ , vol. 53. Singapore: Springer Singapore, 2021, pp. 37–49, data Engineering and Communications Technologies.
  * [25] S. Jalwa, V. Sharma, A. R. Siddiqi, I. Gupta, and A. K. Singh, “Comprehensive and Comparative Analysis of Different Files Using CP-ABE,” in _Advances in Communication and Computational Technology_ , vol. 668. Singapore: Springer Singapore, 2021, pp. 189–198, electrical Engineering.
  * [26] I. Gupta and A. K. Singh, “A Probabilistic Approach for Guilty Agent Detection using Bigraph after Distribution of Sample Data,” _Procedia Computer Science_ , vol. 125, pp. 662 – 668, 2018.
  * [27] K. Kaur, I. Gupta, and A. K. Singh, “Data Leakage Prevention: E-Mail Protection via Gateway,” _Journal of Physics: Conference Series_ , vol. 933, p. 012013, jan 2018.
  * [28] K. Gupta and I. Gupta, “A Comprehensive Study on Architecture, Security issues and Challenges in Cloud Computing,” _International Journal of Scientific & Engineering Research_, vol. 7, no. 12, pp. 128–131, Dec. 2016.
  * [29] A. Nag, A. Kesharwani, B. Sharma, I. Gupta, A. Tiwari, and A. K. Singh, “Potential and extention of internet of things,” in _Second International Conference on Computer Networks and Communication Technologies (ICCNCT)_ , vol. 44. Cham: Springer International Publishing, 2020, pp. 542–551, data Engineering and Communications Technologies.
  * [30] I. Gupta and A. K. Singh, “A Hybrid Technique for the Detection of Data Leakage in Cloud computing Environment,” in _Ist International Conference on Science in Hindi_ , August 2017, vigyan Prakash.
  * [31] I. Gupta and K. Gupta, “Review on Intrusion Detection System Architectures in WSN,” _International Journal of Scientific & Engineering Research_, vol. 7, no. 12, pp. 111–115, Dec. 2016.
  * [32] I. Gupta, H. Mittal, D. Rikhari, and A. K. Singh, “A Multiple Linear Regression based Model for Average Temperature Prediction of A Day,” _CoRR_ , 2022.
  * [33] I. Gupta, N. Singh, and A. Singh, “Layer-based Privacy and Security Architecture for Cloud Data Sharing,” _Journal of Communications Software and Systems (JCOMSS)_ , vol. 15, no. 2, 2019.
  * [34] K. Kaur, I. Gupta, and A. K. Singh, “A Comparative Study of the Approach Provided for Preventing the Data Leakage,” _International Journal of Network Security & Its Applications_, vol. 9, no. 5, pp. 21–33, 2017.
  * [35] I. Gupta, P. K. Yadav, S. Pareek, S. Shakeel, and A. K. Singh, “Auxiliary Informatics System: An Advancement towards a Smart Home Environment,” _CoRR_ , 2022.
  * [36] “Privacy Rights Clearinghouse (PRC),” 2016. [Online]. Available: https://www.privacyrights.org/data-breaches
  * [37] “Chronology of Data Breaches,” 2016. [Online]. Available: http://educationnewyork.com/files/Chronology%20of%20Data%20Breaches%202005%20to%20today.pdf
  * [38] “Security Intelligence: 2016 Ponemon Institute Cost of a Data Breach Study,” 2016. [Online]. Available: https://securityintelligence.com/media/2016-cost-data-breach-study/
  * [39] “CNBC: Data Breach Cost,” 2016. [Online]. Available: http://www.cnbc.com/2016/06/14/cost-of-data-breaches-hits-4-million-on-average-ibm.html
  * [40] R. B. S. (RBS), “2014 Data Breaches –A Billion Exposed Records –A New All Time High,” 2016. [Online]. Available: https://www.riskbasedsecurity.com/2015/02/2014-data-breaches-a-billion-exposed-records-a-new-all-time-high/
  * [41] Identity Theft Resource Center ©Statista 2021, “United States; 2005 to 2020 Sensitive Records Exposed,” 2021. [Online]. Available: https://www.statista.com/statistics/273550/data-breaches-recorded-in-the-united-states-by-number-of-breaches-and-records-exposed/#:~:text=Cyber%20crime%3A%20number%20of%20breaches%20and%20records%20exposed%202005%2D2020&text=In%202020%2C%20the%20number%20of,a%20total%20of%201001%20cases.
  * [42] I. Gupta and A. K. Singh, “Guilty Agent Detection through Probabilistic Analysis,” _CoRR_ , 2022.
  * [43] V. Gautam, C. P. Yadav, R. Verma, I. Gupta, and A. K. Singh, “A Comparative Study on Data Leakage Detection and Prevention,” in _IEEE International Conference on Nanoelectronics, Nanophotonics, Nanomaterials, Nanobioscience & Nanotechnology (5NANO2021)_. IEEE, 2021.
  * [44] I. Gupta and A. K. Singh, “A Probability based Model for Data Leakage Detection using Bigraph,” in _Proceedings of 7th International Conference on Communication and Network Security (ICCNS)_ , ser. ICCNS 2017\. New York, NY, USA: Association for Computing Machinery (ACM), 2017, p. 1–5.
  * [45] I. Gupta, “Malicious Entity Detection for Protecting the Data using Watermarking and Probabilistic Approaches,” _CoRR_ , 2022.
  * [46] I. Gupta and A. K. Singh, “A Confidentiality Preserving Data Leaker Detection Model for Secure Sharing of Cloud Data using Integrated Techniques,” in _2019 7th International Conference on Smart Computing Communications (ICSCC)_. Curtin University, Sarawak Malaysia: IEEE, 2019, pp. 1–5.
  * [47] R. Verma, V. Gautam, C. P. Yadav, I. Gupta, and A. K. Singh, “A Survey on Data Leakage Detection and Prevention,” in _Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020_. SSRN, Elsevier, May 2020, pp. 1–7.
  * [48] A. Acharya, H. Prasad, V. Kumar, I. Gupta, and A. K. Singh, “MACI: Malicious API Call Identifier model to secure the Host Platform,” in _Proceedings of the Seventh International Conference on Mathematics and Computing (ICMC-2021)_ , ser. ICMC 2021, vol. 711. Singapore: Springer Singapore, 2021, advances in Intelligent Systems and Computing.
  * [49] I. Gupta, “A Comparative Study of the Approach Provided for Preventing the Data Leakage,” _Other Topics Engineering Research eJournal_ , vol. 9, no. 5, September 2017. [Online]. Available: https://ssrn.com/abstract=3632686
  * [50] I. Gupta and A. K. Singh, “Data Leakage Detection in Cloud Computing Environment,” _CoRR_ , 2022.
  * [51] A. K. Singh, I. Gupta, R. Verma, V. Gautam, and C. P. Yadav, “A Survey on Data Leakage Detection and Prevention,” in _Proc. Int. Conf. Innov. Comput. Commun._ , 2020.
  * [52] I. Gupta and A. K. Singh, “Solutions for Organizational Data Security and Privacy in Real Environment,” _CoRR_ , 2022.
  * [53] J. Wu, J. Zhou, J. Ma, S. Mei, and J. Ren, “An Active Data Leakage Prevention Model for Insider Threat,” in _2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing_ , 2011, pp. 39–42.
  * [54] M. Hart, P. Manadhata, and R. Johnson, “Text Classification for Data Loss Prevention,” vol. 6794, 07 2011, pp. 18–37.
  * [55] R. Mogull, “Understanding and selecting a data loss prevention solution,” 2021\. [Online]. Available: https://securosis.com/assets/library/publications/DLP-Whitepaper.pdf
  * [56] K. Kaur, I. Gupta, and A. K. Singh, “A Comparative Evaluation of Data Leakage/Loss Prevention Systems (DLPS),” in _Proc. 4th International Conference Computer Science & Information Technology (CS & IT-CSCP)_, 2017, pp. 87–95.
  * [57] I. Gupta and K. Gupta, “Evaluation of Intrusion Detection Schemes in Wireless Sensor Network,” _IOSR Journal of Computer Engineering_ , vol. 18, no. 2, pp. 60–63, Mar-Apr. 2016.
  * [58] N. Kumar, V. Katta, H. Mishra, and H. Garg, “Detection of Data Leakage in Cloud Computing Environment,” in _2014 International Conference on Computational Intelligence and Communication Networks_ , Nov 2014, pp. 803–807.
  * [59] M. Backes, N. Grimm, and A. Kate, “Data Lineage in Malicious Environments,” _IEEE Transactions on Dependable and Secure Computing_ , vol. 13, no. 2, pp. 178–191, March 2016.
  * [60] K. N. Kaur, Divya, I. Gupta, and A. K. Singh, “Digital Image Watermarking Using (2, 2) Visual Cryptography with DWT-SVD Based Watermarking,” in _Computational Intelligence in Data Mining_ , vol. 711. Singapore: Springer Singapore, 2019, pp. 77–86, advances in Intelligent Systems and Computing.
  * [61] P. Papadimitriou and H. Garcia-Molina, “Data Leakage Detection,” _IEEE Transactions on Knowledge and Data Engineering_ , vol. 23, no. 1, pp. 51–63, Jan 2011.
  * [62] A. Shabtai, Y. Elovici, and L. Rokach, _A Survey of Data Leakage Detection and Prevention Solutions_. Springer Science & Business Media, 01 2012.
  * [63] F. Liu, X. Shu, D. Yao, and A. R. Butt, “Privacy-preserving scanning of big content for sensitive data exposure with mapreduce,” in _Proceedings of the 5th ACM Conference on Data and Application Security and Privacy_ , ser. CODASPY ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 195–206.
  * [64] S. Bishop, H. Okhravi, S. Rahimi, and Y. . Lee, “Covert Channel Resistant Information Leakage Protection Using A Multi-Agent Architecture,” _IET Information Security_ , vol. 4, no. 4, pp. 233–247, December 2010.
  * [65] A. Harel, A. Shabtai, L. Rokach, and Y. Elovici, “M-Score: A Misuseability Weight Measure,” _IEEE Transactions on Dependable and Secure Computing_ , vol. 9, no. 3, pp. 414–428, May 2012.
  * [66] X. Shu, D. Yao, and E. Bertino, “Privacy-Preserving Detection of Sensitive Data Exposure,” _IEEE Transactions on Information Forensics and Security_ , vol. 10, no. 5, pp. 1092–1103, May 2015.
  * [67] X. Shu, J. Zhang, D. D. Yao, and W. Feng, “Fast Detection of Transformed Data Leaks,” _IEEE Transactions on Information Forensics and Security_ , vol. 11, no. 3, pp. 528–542, March 2016.
  * [68] X. Shu, J. Zhang, D. Yao, and W.-c. Feng, “Rapid Screening of Transformed Data Leaks with Efficient Algorithms and Parallel Computing,” in _Proceedings of the 5th ACM Conference on Data and Application Security and Privacy_ , ser. CODASPY ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 147–149.
  * [69] X. Shu, J. Zhang, D. Yao, and W.-C. Feng, “Rapid and parallel content screening for detecting transformed data exposure,” in _2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_ , 2015, pp. 191–196.
  * [70] C. Maldonado, “Data Leakage Detection Using Dynamic Data Structure and Classification Techniques,” _INGE CUC_ , vol. 11, no. 1, pp. 79–84, 01 2015\.
  * [71] S. Xun and D. Yao, “Data Leak Detection as a Service,” vol. 106, 09 2013, pp. 222–240.
  * [72] S. Sodagudi and R. R. Kurra, “An Approach to Identify Data Leakage in Secure Communication,” in _Proceedings of 2nd International Conference on Intelligent Computing and Applications_. Singapore: Springer Singapore, 2017, pp. 31–43.
  * [73] K. Kaur, I. Gupta, and A. K. Singh, “E-Mail Protection System to Prevent Data Leakage,” _Vigyan Prakash_ , vol. 16, pp. 30–36, 2018.
  * [74] A. Mohsen Nia, S. Sur-Kolay, A. Raghunathan, and N. K. Jha, “Physiological Information Leakage: A New Frontier in Health Information Security,” _IEEE Transactions on Emerging Topics in Computing_ , vol. 4, no. 3, pp. 321–334, July 2016.
  * [75] J. Croft and M. Caesar, “Towards Practical Avoidance of Information Leakage in Enterprise Networks,” in _Proceedings of the 6th USENIX Conference on Hot Topics in Security_ , ser. HotSec’11. Berkeley, CA, USA: USENIX Association, 2011, pp. 7–7.
  * [76] J. Jung, A. Sheth, B. Greenstein, D. Wetherall, G. Maganis, and T. Kohno, “Privacy Oracle: A System for Finding Application Leaks with Black Box Differential Testing,” 01 2008, pp. 279–288.
  * [77] S. Alneyadi, E. Sithirasenan, and V. Muthukkumarasamy, “A Survey on Data Leakage Prevention Systems,” _Journal of Network and Computer Applications_ , vol. 62, pp. 137 – 152, 2016.
  * [78] B. Hauer, “Data and Information Leakage Prevention Within the Scope of Information Security,” _IEEE Access_ , vol. 3, pp. 2554–2565, 2015.
  * [79] S. Alneyadi, E. Sithirasenan, and V. Muthukkumarasamy, “Detecting Data Semantic: A Data Leakage Prevention Approach,” 08 2015, pp. 910–917.
  * [80] T. Wüchner and A. Pretschner, “Data Loss Prevention Based on Data-Driven Usage Control,” in _2012 IEEE 23rd International Symposium on Software Reliability Engineering_ , 2012, pp. 151–160.
  * [81] A. Squicciarini, S. Sundareswaran, and D. Lin, “Preventing Information Leakage from Indexing in the Cloud,” in _2010 IEEE 3rd International Conference on Cloud Computing_ , 2010, pp. 188–195.
  * [82] J. Linwood, T. Jaeger, R. Perez, R. Sailer, L. van Doorn, and R. Cáceres, “Trusted Virtual Domains: Toward Secure Distributed Services,” in _Proceedings of 1st IEEE Workshop on Hot Topics in System Dependability (Hot-Dep’05)_ , 06 2005.
  * [83] W. J. Blanke, “Data Loss Prevention Using An Ephemeral Key,” in _2011 International Conference on High Performance Computing Simulation_ , 2011, pp. 412–418.
  * [84] T. M. S. Yoshihama, T. Mishina, “Web-based Data Leakage Prevention,” in _Presented at IWSEC_ , 2010.
  * [85] K. Borders and A. Prakash, “Quantifying Information Leaks in Outbound Web Traffic,” in _2009 30th IEEE Symposium on Security and Privacy_ , 2009, pp. 129–140.
  * [86] C. H. Suen, R. K. Ko, Y. S. Tan, P. Jagadpramana, and B. S. Lee, “S2Logger: End-to-End Data Tracking Mechanism for Cloud Data Provenance,” in _2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications_ , 2013, pp. 594–602.
  * [87] W. Boehmer, “Analyzing Human Behavior Using Case-Based Reasoning with the Help of Forensic Questions,” in _2010 24th IEEE International Conference on Advanced Information Networking and Applications_ , 2010, pp. 1189–1194.
  * [88] Y. Shapira, B. Shapira, and A. Shabtai, “Content-based Data Leakage Detection Using Extended Fingerprinting,” _arXiv_ , 2013.
  * [89] Y. Lindell and B. Pinkas, “Privacy Preserving Data Mining,” _Journal of Cryptology_ , vol. 15, pp. 177–206, 2002.
  * [90] J. Marecki, M. Srivatsa, and P. Varakantham, “A Decision Theoretic Approach to Data Leakage Prevention,” in _2010 IEEE Second International Conference on Social Computing_ , 2010, pp. 776–784.
  * [91] J. M. Gómez-Hidalgo, J. M. Martín-Abreu, J. Nieves, I. Santos, F. Brezo, and P. G. Bringas, “Data Leak Prevention through Named Entity Recognition,” in _2010 IEEE Second International Conference on Social Computing_ , 2010, pp. 1129–1134.
  * [92] M. Sokolova, K. El Emam, S. Rose, S. Chowdhury, E. Neri, E. Jonker, and L. Peyton, “Personal Health Information Leak Prevention in Heterogeneous Texts,” in _Proceedings of the Workshop on Adaptation of Language Resources and Technology to New Domains_ , ser. AdaptLRTtoND ’09. USA: Association for Computational Linguistics, 2009, p. 58–69.
  * [93] J. J. Parekh, K. Wang, and S. J. Stolfo, “Privacy-Preserving Payload-Based Correlation for Accurate Malicious Traffic Detection,” in _Proceedings of the 2006 SIGCOMM Workshop on Large-Scale Attack Defense_ , ser. LSAD ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 99–106.
  * [94] V. R. Carvalho, R. Balasubramanyan, and W. W. Cohen, “Information Leaks and Suggestions: A Case Study using Mozilla Thunderbird,” in _Sixth Conference on Email and Anti-spam (CEAS)_ , California USA, 2009.
  * [95] P. Zilberman, S. Dolev, G. Katz, Y. Elovici, and A. Shabtai, “Analyzing Group Communication for Preventing Data Leakage Via Email,” in _Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics_ , 2011, pp. 37–41.
  * [96] V. R. Carvalho and W. W. Cohen, “Preventing Information Leaks in Email,” in _Presented at SDM_ , 2007, pp. 68–77.