# Data privacy for Mobility as a Service

Zineb Garroussi<EMAIL_ADDRESS>Antoine Legrain
<EMAIL_ADDRESS>Sébastien Gambs<EMAIL_ADDRESS>Vincent
Gautrais<EMAIL_ADDRESS>Brunilde Sansò
<EMAIL_ADDRESS>
###### Abstract

Mobility as a Service (MaaS) is revolutionizing the transportation industry by
offering convenient, efficient and integrated transportation solutions.
However, the extensive use of user data as well as the integration of multiple
service providers raises significant privacy concerns. The objective of this
survey paper is to provide a comprehensive analysis of the current state of
data privacy in MaaS, in particular by discussing the associated challenges,
existing solutions as well as potential future directions to ensure user
privacy while maintaining the benefits of MaaS systems for society.

###### keywords:

Data privacy , Location-based Services , Mobility as a Service (MaaS) ,
Privacy by design , Ride-hailing , Ridesharing.

[1]organization=Polytechnique Montreal, addressline=2900 Edouard-Montpetit
Blvd, University of Montreal Campus, 2500 Polytechnique Road, postcode=H3T
1J4, city= Montreal, country=Canada

[2]organization=Interuniversity Research Center on Enterprise Networks,
Logistics and Transportation (CIRRELT), addressline=Andre-Aisenstadt Building,
University of Montreal, P.O. Box 6128, postcode=H3C 3J7, city= Montreal,
country=Canada

[3]organization=University of Quebec in Montreal, Computer science department,
addressline=2098 Rue Kimberley, postcode=H3C 3P8, city= Montreal,
country=Canada

[4]organization=Group for Research in Decision Analysis (GERAD),
addressline=HEC Montreal, 3000, Cote-Sainte-Catherine Road, postcode=H3T 2A7,
city= Montreal, country=Canada

[5]organization=CRDP - Faculty of Law - University of Montreal,
addressline=3101 Ch de la Tour, postcode=H3T 1J7, city= Montreal,
country=Canada

##  1 Introduction

The growing urbanization of our society, along with the increasing
availability of mobile technology, is driving the need for alternative
mobility solutions. For instance, according to the United Nations, 55% of the
world’s population now live in urban areas, and this number is expected to
grow to 68% by 2050 [1]. Overall combined with population growth, another 2.5
billion people are thus expected to join cities over the next 30 years. Among
possibles approaches, we focus on mobility-as-a-service (MaaS) solutions in
this paper.

More precisely, MaaS uses information and communication technologies to
integrate real-time traffic data with existing transportation modes, such as
public transport, cars, bikes and rideshares. Thus, MaaS enables optimal route
display and one-stop registration, reservations and payments through apps like
Apple Pay or credit cards [2], thus enhancing convenience.

Originating in Finland in 2016, MaaS uses advanced technologies like IoT to
optimize transportation [3, 4]. Since then, MaaS has revolutionized the
transportation system in a way that no one could have even imagined a decade
ago.

According to Statistica, the global MaaS market is estimated to be worth over
$230 billion by the end of 2025 (at a CAGR of 32.6%) [5]. Its success is also
measured by factors like adoption rate and environmental impact, showing its
ability to encourage sustainable travel and economic growth [6]. In
particular, MaaS helps to reduce private car usage, mitigates climate change,
alleviates traffic congestion and facilitates multi-modal travel with ease. It
can also foster people-focused communities, boost local industries and aids
tourism by improving access to remote areas [7, 8].

However, the rapid expansion of MaaS has led to an increasing reliance on
personal data to deliver seamless and efficient transportation solutions [9].
With this growth comes a pressing need to address privacy concerns and
implement recognized privacy practices, such as privacy-by-design, privacy-by-
default as well as privacy impact assessments [10]. In particular, the right
to privacy is now viewed as a fundamental human right in many national
constitutions, international treaties, and the Universal Declaration of Human
Rights, and is gaining increased prominence in the modern data-driven
landscape [11, 12]. Key privacy regulations, like the EU’s General Data
Protection Regulation, provide individuals control over their personal
information, shielding them from unwarranted intrusions [13]. Yet, the
widespread utilization of artificial intelligence and data mining for handling
massive personal data quantities is kindling new concerns [14].

These concerns are tied to the growing data generation and collection by
governments and corporations, and the subsequent questions about ethical data
use and access. Consequently, there are calls for robust privacy protections
mechanisms and regulations, to ensure individuals’ control over their personal
information. Privacy by Design, a concept introduced by Ann Cavoukian in 1995,
advocates for proactive embedding of privacy into technology systems, rather
than as a later addition [15]. This principle promotes seamless and
transparent user privacy, requiring no additional user action [16]. Nowadays
acknowledged worldwide, it is recognized as a best practice in personal data
protection in today’s digital world [17].

The approach’s seven principles [15, 18], which include for instance proactive
privacy, privacy as default and user respect, aim to anticipate privacy
issues, maintain system usability as well as ensuring user control over their
personal data.

In particular, this proactive privacy incorporation guides our discussion on
data privacy within the evolving MaaS ecosystem. To realize this, privacy-
enhancing technologies have emerged as part of the solution by aiming to
reduce personal data collection, processing and sharing. These technologies
help to implement privacy-by-design principles [19], considering privacy at
every design stage. Examples of privacy-enhancing technologies include
techniques such as encryption and anonymization [10, 20].

In the context of privacy protection, the concept of privacy-by-default is
crucial. It is enshrined in the EU’s General Data Protection Regulation (GDPR)
and is aligned with privacy-by-design. According to Article 25 of the GDPR,
controllers are required to process only the necessary personal data, ensuring
limited and proportionate data processing while respecting individuals’
privacy rights. The GDPR also obliges controllers to implement measures for
data security, safeguarding against unauthorized processing, accidental loss,
destruction or damage.

Finally, Privacy Impact Assessments (PIAs) play a significant role in
evaluating the potential impact of projects, policies or systems on
individuals’ privacy rights [21]. These assessments, recommended by the Office
of the Privacy Commissioner of Canada (OPC), identify privacy risks and
propose appropriate mitigation measures. Although not always legally mandated
in Canada, PIAs are considered best practices for demonstrating compliance
with data protection laws and some Canadian provinces like British Columbia
require their implementation [22].

Focusing on a specific type of risk, as the popularity of MaaS applications
increases, there’s a surge in the collection, storage, sharing and tracking of
real-time, location-based data. More precisely, in the MaaS ecosystem, users
engage with providers via smartphones, which enables continuous tracking of
location and behavior for profiling and other purposes [23]. This collection
includes for example users’ travel patterns, destinations and modes of
transportation. While this information can be utilized for various purposes
such as marketing and advertising, it may intrude upon users’ privacy [24]. In
addition, location data, considered to be sensitive by nature, can reveal
personal information such as religious beliefs or political affiliations,
hence requiring heightened protection. EU data protection law recognizes these
risks and demands specific protections for special categories of personal
data, which may pose high risks when processed. While, MaaS providers collect
location data to personalize subscriptions and analyze travel patterns, the
danger lies in the potential for unrestricted access leading to the unfair
accumulation of movement profiles, and the “datification” of patterns for the
purpose of shaping and selling personalized goods and services [25].

In particular, the widespread use of technology and Internet has facilitated
data collection and storage for organizations, but it has also elevated the
risk of data breaches and unauthorized access to sensitive information [26,
23].

MaaS applications, given their ability to collect real-time, detailed data,
significantly elevate the privacy risks traditionally associated with data
collection. MaaS systems are susceptible to various attacks that threaten
personal data, re-identification, inference and eavesdropping attacks.
Additionally, malware can potentially access a user’s device and steal data,
while social engineering attacks deceive users into sharing information. Thus,
safeguarding against these attacks is vital for preserving the privacy and
security of personal data in MaaS systems [27].

#### Scope of the Paper

This paper aims to present a comprehensive survey of the current state of data
privacy within the MaaS ecosystem. More specifically, the scope of this survey
encompasses several key areas, including the identification of major privacy
challenges in MaaS, such as risks related to personal information, location
data and third-party data sharing. Furthermore, it examines existing privacy-
enhancing techniques employed in MaaS systems, such as anonymization methods,
cryptographic solutions, blockchain-based approaches and privacy-preserving
machine learning. Additionally, the paper delves into the legal and regulatory
landscape governing data privacy in MaaS, with a focus on international
privacy standards and legislation. Finally, this survey offers recommendations
for enhancing data privacy in MaaS and identifies potential future research
directions in this critical field.

#### Methodology

To gather relevant papers on the subject of data privacy in the MaaS
ecosystem, a systematic and comprehensive approach was adopted. Multiple
academic databases and search engines, such as IEEE Xplore, Google Scholar and
ScienceDirect were utilized to identify papers that discussed the various
aspects of data privacy within MaaS. Key search terms and phrases, including
”Mobility as a Service”, ”data privacy”, ”privacy challenges”, ”privacy-
preserving techniques” and ”legal and regulatory in MaaS”, were employed to
ensure the thoroughness of the search process. The collected literature was
then carefully scrutinized for quality and relevance, with an emphasis on
studies that provided insights into the current state of data privacy in MaaS,
existing privacy-enhancing techniques as well the regulatory environment
surrounding this domain.

#### Paper Organization and Structure

The remainder of the paper is structured as follows. First, Section 2 delves
into the state of data in MaaS, with an emphasis on location-based services
data and related collection and processing methods. Afterwards, Section 3
explores the legal and regulatory framework for data privacy in MaaS,
addressing local and international data protection laws and compliance
challenges. In Section 4, the classification of data privacy methods in MaaS
is presented, discussing related works and identifying limitations in the
existing state-of-the-art. Then, Section 5 highlights future research
directions in data privacy for MaaS, such as game theory, edge computing and
evaluation methods. Finally, Section 6 concludes the paper.

##  2 Data in MaaS

Data, consisting of stored electronic information, symbols, or signals, is
essential for the success of Mobility as a Service (MaaS) [28]. In particular,
MaaS providers amalgamate a vast array of transport data and APIs from
multiple operators to allow customers access to information on mobility
options, reservation and payment. APIs manage interactions between web-based
services, constituting the foundation for MaaS providers’ operations [29].
Through API sharing, MaaS providers present a unified digital platform for
planning, booking, paying and utilizing transportation while also creating the
app and website that function as the main customer-facing interface [30]. MaaS
applications manage directly several categories of sensitive data, which
include but are not limited to Personal Identifiable Information (PII) and
location data.

###  2.1 Personal Identifiable Information

MaaS platforms gather a broad spectrum of PII from their users, including but
not limited to names, physical addresses, email addresses and phone numbers,
which are typically required for account creation. Other identifying
information such as IP addresses and device identifiers are also often
collected. Furthermore, payment data like credit or debit card details and
billing addresses are necessary for transactional purposes. In case of a
breach, such data is susceptible to lead to identity theft, financial fraud as
well as other forms of cybercrime. Additionally, MaaS platforms may collect
travel history, such as the details of journeys taken, preferred routes,
frequency of travel and times of travel. This kind of PII can be used to
tailor services, suggest personalized routes and enhance overall user
experience. User preference data, such as preferred modes of transport or
payment methods, can also be recorded to facilitate ease of use and
personalized recommendation provided to a user. Additionnally, biometric data
such as facial recognition or fingerprint information, used for identity
verification and access control, can also be sensitive as it can be used for
surveillance or other forms of privacy intrusion. Finally, demographic
information like age, gender and occupation may be obtained to aid in broad-
level analysis and segmentation [10].

###  2.2 Location data

Location-based services (LBS) in MaaS refers to the use of location to provide
users with personalized and context-aware mobility options and services based
on their current location and travel preferences. LBS can include features
such as real-time transit updates, trip planning and optimization, location-
based promotions and discounts and on-demand ride-hailing services [31].

For instance, a MaaS platform might employ LBS to provide the most practical
and economical travel options depending on a user’s present location, the time
of day, and preferred mode of transportation. This could include recommending
public transit options with real-time updates on delays or suggesting an on-
demand ride-hailing service for last-mile connectivity [32]. However, the use
of LBS in MaaS presents privacy and security concerns because it involves the
gathering and use of location data. Thus, appropriate safeguards and user
consent mechanisms should be in place to ensure the responsible use of
location data in MaaS services. In particular, location data can reveal
sensitive information about users in several ways. First, it can correspond to
real-time location information, as well as location history, which can expose
users’ movements, habits and routines, potentially leading to stalking,
harassment or other malicious activities [33]. Second, location data can
reveal health-related information, such as medical conditions, medication
regimes and mobility limitations, which can be exploited for discrimination,
targeted advertising or other harmful purposes [34].

Privacy is a key concern in both continuous and snapshot LBS, as these
services rely on the collection and use of sensitive location data. In
continuous LBS, user location is tracked continuously over time, which can
lead to more severe privacy risks. It can be used to create detailed movement
profiles of individuals, which can reveal sensitive information about their
habits and routines. This information could be misused for stalking,
surveillance and other malicious purposes [35]. To reduce the privacy exposure
in continuous LBS, authors in [35] proposed a privacy method based on multi-
level caching mechanism and spatial $k$-anonymity. A detailed discussion on
k-anonymity will be presented in the section 4. In snapshot LBS, user location
data is obtained at a specific point in time, which may seem less intrusive
than continuous tracking. However, snapshot LBS can still pose privacy risks
if the position of a user is collected and used without appropriate safeguards
and user consent. For example, location-based advertising and recommendations
may use user location data to provide personalized offers or promotions, but
users may not be aware of how their location data is being used or shared with
third-party advertisers.

To address these privacy concerns, it is important to implement appropriate
privacy and security measures in both continuous and snapshot LBS, such as
data anonymization, data minimization and user consent mechanisms. [36] aims
to address the issue of privacy leakage from location data by obfuscating the
temporal information in trajectory data.

The algorithm uses a randomized time transformation method to create a time-
obfuscated trajectory that preserves the spatio-temporal properties of the
original trajectory while hiding the exact time information. The authors
evaluated the proposed algorithm using real-world datasets and compared it to
existing trajectory privacy protection methods. The results showed that the
proposed algorithm achieved better privacy protection performance while
maintaining a high level of data utility. In [37] a proposed mechanism
utilizes a deep learning-based approach to extract the location features from
the original location data, which is then used to generate a privacy-
preserving representation of the location data. This privacy-aware
representation ensures that direct identification from the location data
becomes unfeasible, thereby safeguarding user privacy. Importantly, despite
this transformation, the resultant representation retains critical
characteristics of the original data. This implies that specific tasks, such
as location-based service recommendations or mobility pattern analysis, remain
feasible with this anonymized data. The strength of this approach lies in its
ability to strike a balance between utility and privacy, ensuring that
essential features for particular tasks are preserved even as identifiable
details are obscured.

[38] proposes a scheme for preserving the location privacy of users in LBS
based on a novel combination of $k$-anonymity and differential privacy
techniques to achieve a better trade-off between privacy and utility. The
paper discusses the privacy threats in LBS and evaluates the proposed scheme
through simulations. [39] proposes an enhanced privacy-preserving framework
for LBS. The proposed framework is based on a double cloaking region scheme,
which utilizes two cloaking regions to protect user location information. It
should be noted that a comprehensive discussion on differential privacy and
cloaking will be presented in section 4. The framework also incorporates
supplementary information constraints to enhance the security and privacy of
user data. Additionally, regulators and policymakers should establish clear
guidelines and regulations around the collection and use of location data in
LBS to protect users’ privacy rights.

###  2.3 The Role of Data Privacy Across Different MaaS Integration Stages

As MaaS becomes increasingly complex and integrated, with an evolving
ecosystem of stakeholders and services, it becomes critical to manage the
growing challenges associated with data privacy. More precisely, each level of
integration in MaaS brings a unique set of data privacy issues. Recognizing
these challenges, and developing comprehensive strategies to address them,
will play a vital role in shaping the future of MaaS [40]. Figure 1 provides a
visual representation of the MaaS ecosystem, highlighting the different
stakeholders involved in data exchange, the services provided by the platform
as well as the Privacy Assurance Framework layers that ensure data privacy and
protection.

Figure 1: MaaS Ecosystem and Privacy Assurance Framework

The taxonomy developed by the research team at Chalmers University of
Technology in Sweden holds significance in comprehending and tackling data
privacy issues within MaaS [40]. As MaaS evolves through its five stages, from
Level 0 (No integration) to Level 4 (Policy integration), the degree of data
privacy concerns escalates in tandem. For instance, a Level 1 (Information
Integration) application can function without creating user profiles, thus
minimizing data privacy risks. However, as MaaS advances to Level 2
(Integration of search/reservation/payment), more personal data is required
for tasks such as booking and payment, subsequently increasing privacy
concerns. Level 3 (Service Delivery Integration): Further integration of all
modes of transportation and movement will be promoted. Regardless of public or
private, not only transportation but also sharing services will be added, and
all means of transportation will be available. Platforms and subscription
services that allow unlimited rides on mobile services within a certain area
are also envisioned. It is the integration of service delivery. Level 4
(Policy integration): In addition to efforts at the business level, support
from the national and local governments can be obtained. By linking MaaS with
policies such as tourism development and smart cities, it is expected to solve
social issues such as alleviating traffic congestion and improving convenience
for the elderly. This classification is crucial for understanding the
implications of data privacy as MaaS progresses and becomes more integrated,
enabling the development of effective strategies to protect user information
while maintaining the convenience and efficiency that MaaS aims to provide.

Table LABEL:tab:maas_data_summary provides a brief summary of the types of
data collected from users, transport providers, city authorities and third-
party services at each level of the MaaS taxonomy. In the table each column is
associated to one of the level of the taxonomy, including No Integration Level
(Level 0), Level 1 (Information Integration), Level 2 (Integration of
Search/Reservation/Payment), Level 3 (Service Delivery Integration) and Level
4 (Policy Integration). Each row of the table corresponds to a stakeholder,
and the cells provide a summary of the types of data that may be collected
from each stakeholder at each level. This table can help MaaS providers
understand various types of data that can be collected at each level.

Table 1: MaaS data collection aggregated by level and stakeholder. |  |  |  |  |
---|---|---|---|---|---
|  Level 0 (No integration) |  Level 1 (Information Integration) |  Level 2 (Integration of search & reservation/payment) |  Level 3 (Service Delivery Integration) |  Level 4 (Policy integration)
Users |  No data collected by MaaS providers since there is no platform in place |  User profile such as name, email, and phone number for registration and authentication purposes |  User profile data including payment information such as credit card details for payment processing, location data for real-time traffic updates and directions, and trip data from multiple transportation providers to offer more comprehensive transportation services. |  |
User preferences and personal information for personalized services may also be collected. |  Travel behavior and preferences to optimize transportation services. |  |  |  |
Advanced location data, such as real-time data from sensors and GPS, and data from external sources, such as weather and events, may also be collected to inform transportation planning. |  |  |  |  |
Data on transportation infrastructure, such as road conditions and public transit schedules, may also be collected to provide real-time information to users. |  Sensitive personal information, such as biometric data, medical information, and travel history, to provide a complete end-to-end travel experience for users. Data from multiple transportation providers and infrastructure sources to offer a seamless and efficient travel experience. Analytics data, such as usage data and user behavior, to improve the MaaS platform and services. |  |  |  |
Transport Providers |  Data collected directly by transport providers may include location data, trip data and passenger data to improve service offerings and route optimization |  Basic information such as routes, schedules and fares for basic transportation options |  More detailed information on routes, schedules and fares for multiple transportation options |  Location data, trip data, and passenger data for improved service offerings and route optimization. Real-time data from sensors and GPS for improved service delivery. |  Location data, trip data, and passenger data for improved service offerings and route optimization. Real-time data from sensors and GPS for improved service delivery.
City Authorities |  Data collected directly by city authorities may include traffic data, public transit data, and infrastructure data to inform transportation planning and policy |  No data collected |  Traffic data, public transit data, and infrastructure data to inform transportation planning and policy. |  Traffic data, public transit data, and infrastructure data to inform transportation planning and policy. |  Traffic data, public transit data, and infrastructure data to inform transportation planning and policy. Data on environmental impact and energy use to inform policy decisions.
Third-Party Services |  No data collected by MaaS providers since there is no platform in place |  No data collected |  Marketing data, user data to deliver targeted ads and promotions. Data on reservations and payments to facilitate the transportation booking process. |  Analytics data, such as usage data and user behavior, to improve the MaaS platform and services. |  Marketing data, user data to deliver targeted ads and promotions. Data on policy compliance to inform decision-making.

##  3 Privacy Laws and Regulations in MaaS

Privacy rules are crucial in MaaS. This later makes heavy use of people’s data
for route optimization, pricing and quality of service. This highlights the
importance of privacy laws and regulations for data collection and use,
ensuring user trust [41]. This section presents these rules in MaaS, including
some policies, their impact, and implications for service providers.

###  3.1 Global Overview of Privacy Laws and Regulations in MaaS

Privacy laws and rules are very important for MaaS. They create a framework to
protect data and user privacy. One of the most important laws is the General
Data Protection Regulation (GDPR), which started in Europe. This law has had a
big impact on MaaS providers globally [11] [42]. It sets out key principles
including data minimization, obtaining explicit user consent before data
collection, and offering users the ’right to be forgotten’. For example, a
leading MaaS provider in Germany had to extensively revise its data collection
procedures to ensure explicit user consent, signifying the profound influence
of GDPR on the MaaS industry in Europe.

Simultaneously, in the United States, the California Consumer Privacy Act
(CCPA) sets stringent rules for companies handling personal data. For example,
the CCPA requires MaaS providers to disclose their data collection, selling,
and sharing practices to users. It has led to substantial changes in the
operations of MaaS providers, exemplified by a notable provider in California
that had to significantly modify its data handling practices to ensure
compliance with the CCPA [43].

Beyond these two significant regulations, other regional or national privacy
laws also impact MaaS. For example, the Personal Information Protection and
Electronic Documents Act (PIPEDA) in Canada [44] is currently being re-
evaluated (Bill C-27) to reflect the increasing obligations of companies
handling personal data [45] . The Personal Data Protection Act (PDPA) in
Singapore [46] is also an important text dealing with the growing importance
of privacy rules. While broadly following the principles of GDPR and CCPA,
these regulations introduce unique elements. For instance, Bill C-27
emphasizes accountability, requiring organizations to appoint a data
protection officer, while PDPA in Singapore stresses the protection of
children’s data.

Furthermore, PIPEDA governs how organizations collect, use, and disclose
personal information in the course of commercial activities. To protect MaaS
data under PIPEDA, users are encouraged to exercise their rights and be
cautious about the personal information they share online. Privacy-enhancing
tools like VPNs and encrypted messaging services are also recommended.

In Quebec, the Access to Information Commission issued a reminder in September
2021 about the enactment of certain provisions of Bill 25, the Act to
modernize legislative provisions relating to the protection of personal
information in the private sector. This reform adjusts the rules for
protecting personal information in Quebec to address the challenges posed by
the digital and technological environment. Bill 25 imposes additional
obligations on private businesses and promises increased protection of
personal information, new rights for citizens, and more responsible and
transparent management of personal information. The changes will be phased in
over three years, until 2024 [47].

###  3.2 The Role of Privacy Impact Assessments (PIAs)

Among the more demanding measures found in the aforementioned laws, several
require, expressly or not, that companies draw up PIA to demonstrate the
diligence employed in the management of personal data. PIAs are a fundamental
tool within the global privacy regulatory landscape and have significant
implications for how MaaS providers handle personal data. These assessments
involve a systematic review of the potential effects of a project, policy, or
system on privacy rights, paving the way for identifying privacy risks and
implementing appropriate measures to mitigate them [21]. Though not
universally legally mandated, PIAs are considered best practices in many
jurisdictions. For example, the Office of the Privacy Commissioner of Canada
(OPC) advocates for PIAs to determine how new or modified initiatives might
impact personal privacy. In some provinces like British Columbia, PIAs are
legally required for certain contexts [22]. The same is true of new laws
passed in Quebec [47].

Within the MaaS context, PIAs enable service providers to scrutinize and
address the privacy implications of their offerings proactively. PIAs ensure
the identification and mitigation of potential privacy risks before they
materialize, thus creating a preventative approach to privacy protection. This
can involve scrutinizing data collection, storage, and processing procedures,
assessing potential impacts on user privacy, and instituting necessary
safeguards.

Implementing PIAs also allows MaaS providers to demonstrate their dedication
to privacy, fostering trust among users and aiding in maintaining regulatory
compliance. Moreover, PIAs can guide the design and deployment of MaaS
services, promoting the integration of privacy measures from the onset. This
preventive and proactive strategy aligns with the ”privacy by design” approach
advocated by leading data protection frameworks like the GDPR [18].

###  3.3 Implications for MaaS Providers

The far-reaching privacy laws and regulations impose a profound influence on
the operations, business models, and consumer relations of MaaS providers
[48]. With the increasing digitization of services, MaaS providers rely
heavily on user data to enhance their services and offer personalized
experiences. As a result, these providers must navigate a complex landscape of
laws that protect user privacy while ensuring their services remain
competitive and user-centric [49].

One of the primary impacts of these laws is on the operations of MaaS
providers. Laws such as GDPR and CCPA mandate strict guidelines on data
collection, usage, and storage.

The data handling procedures of MaaS providers frequently need to undergo
extensive adjustments in order to comply with these regulations. These
adjustments may involve implementing systems to collect and manage user
consent, creating tools for data minimization, and enhancing data security
architecture [49].

Additionally, these restrictions significantly impact the business models of
MaaS providers. Laws on data collection and usage can force providers to
rethink their strategies for data-driven monetization. The business models
must balance the need for data to improve services with the imperative of user
privacy. This balance requires careful planning and execution to ensure both
profitability and compliance [9]. This is especially true in light of the
severe consequences for breaking the law, outlined in the GDPR, Bill C-27 in
Canada, and L25 in Quebec. These laws also strongly affect how MaaS companies
interact with their customers. Privacy laws often ask for openness about how
data is used, which can help create trust with customers. By demonstrating
their compliance with these laws, MaaS providers can portray themselves as
responsible custodians of user data, which can help in fostering stronger
consumer relations [50].

Despite the potential benefits, compliance with these laws presents
significant challenges for MaaS providers. The laws often differ across
jurisdictions, which means that providers operating in multiple regions need
to navigate a complex, varied landscape of privacy regulations. Moreover, as
these laws evolve over time, maintaining compliance can be an ongoing
challenge that requires constant vigilance and adaptability [49].

Indeed, navigating the evolving landscape of privacy laws and regulations is a
significant challenge for MaaS providers. However, this challenge also
presents an opportunity for innovation and improvement in data privacy
practices. In this context, the classification of data privacy methods emerges
as a critical area of focus. By understanding and implementing these methods,
MaaS providers can not only ensure compliance with laws but also offer a
secure, privacy-respecting service to users. The subsequent section delves
into this topic, providing a comprehensive classification of data privacy
methods in MaaS.

##  4 Classification of data privacy methods in MaaS

Given the pivotal role of data privacy in MaaS [51], substantial research has
been devoted to the development of various privacy-preserving techniques.
These methods encompass anonymization, cryptography, differential privacy,
distributed learning methods and blockchain. In the subsequent subsections, we
will discuss each of these methods, provide an overview of their current state
of the art, and compare their effectiveness in preserving privacy. Figure 2
depicts the proposed classification conducted by several journal/conference
papers.

Figure 2: Taxonomy of Privacy-preserving methods for MaaS

###  4.1 Anonymization methods

Within the realm of data anonymization, k-anonymity and mix zones stand out as
two frequently utilized strategies [52], [53], [54], [55].

The k-anonymity method is a privacy protection technique that ensures that a
user’s location cannot be distinguished from $k-1$ other users using
generalization and suppression algorithms. To achieve this, a trusted third
party (TTP) operates the location privacy server that anonymizes user
locations. When a user sends a location query, the TTP calculates a set of $k$
users and provides an obfuscation area containing $k$ positions, including the
querying user’s location. This approach is useful for applications that do not
require true or pseudo-identity, such as finding nearby restaurants or
notifying a user of sale prices. However, it is not useful when identity
information is necessary for the LBS to provide its services [56].

K-anonymization has been extended in two ways. The first involves using
multiple distributed servers or peer-to-peer communication instead of a single
trusted anonymizer, while the second involves constraining the users included
in $k$ based on contextual knowledge that an adversary may possess.
P-sensitivity guarantees that the key attributes have at least $p$ different
values within the $k$ user set, while l-diversity ensures that the user’s
location is unidentifiable from a set of $l$ different physical locations.
Historical k-anonymity provides guarantees for moving objects.

Mix-zones are a privacy protection technique that does not require user
identity information. It involves continually changing a user’s pseudonym
within a mix-zone to maintain privacy [57]. This technique has been applied in
various applications, including location privacy in vehicular networks [58]
and mobile crowd sensing [59].

The limitations of anonymization methods, discussed in [60], arise from the
compromise between privacy and data utility. Techniques like k-anonymity,
p-sensitive k-anonymity, and l-diversity lack comprehensive privacy
protection, while t-closeness sacrifices attribute correlations for complete
privacy. Computational strategies for these methods often involve
generalization and suppression, distorting numerical attributes into
categorical ones. The absence of a clear procedure for t-closeness further
complicates the issue. Mix-zones, similarly, encounter challenges in balancing
anonymity and meaningful data representation.

Addressing the challenges in implementing k-anonymity and mix zones, as well
as the call for more context-aware solutions, numerous recent studies have
been undertaken. For instance, PrivateRide in [52] addresses the issue of
location privacy by providing user anonymity and hiding the origin and
destination of the trip from the service provider (SP). Instead of sharing
this sensitive information, an approximate origin and destination area are
sent to the SP. The precise locations are only revealed to the driver once
they are matched with a passenger. Al-Momani [53] proposed a privacy scheme in
driverless cabs service (iRide) with analogy with PrivateRide. iRide relies on
isolated execution environments; Intel SGX to anonymize pick-up and drop off
locations. [54] presented a protocol that employs RNE to calculate shortest
distances effectively. Their approach utilizes hash functions that maintain
certain properties, allowing the service provider (SP) to not only calculate
distances between riders and drivers, but also select the nearest driver.
Consequently, they eliminate the requirement for an additional cryptographic
server. [55] proposes a privacy-enhancing technique based on the selection of
points of interest (POIs) for pick-up and drop-off locations. This technique
involve selecting POIs that are more crowded or more generic in order to
obfuscate the exact location and identity the passengers. [61] studied the
matching performance loss in cloaking methods and propose to maximize the
social welfare (minimize the overal pick-up distances) with pricing scheme to
compensate loss of priority matching accuracy.

###  4.2 Cryptography or Private computation

Cryptography has the potential to enhance data privacy in MaaS by protecting
sensitive information, reducing the risk of data breaches, and preserving user
privacy [62], [63], [64], [65], [66], [67], [68], [69], [70], [71] . However,
there are also some potential drawbacks to consider. For instance, private
computation can require additional computational resources, which may slow
down processing time and reduce system performance. Implementing private
computation can also be complex and require expertise.

Private computation’s privacy guarantees may be constrained by factors such as
the accuracy of data models and the quality of the data used. Furthermore, it
may impede sharing data with third parties, which can be a disadvantage for
MaaS applications reliant on data exchange. Finally, implementing private
computation can be costly, particularly for small businesses or startups that
may lack the resources to invest in privacy-preserving technologies [72].
Among cryptography methods:

Partially homomorphic encryption allows for computation on only one type of
operation (either addition or multiplication) while keeping the ciphertext
encrypted [73]. Somewhat homomorphic encryption enables computation on both
addition and multiplication but has a limited number of operations that can be
performed. Fully homomorphic encryption enables arbitrary computation on
encrypted data [74].

Oblivious Transfer is a protocol in cryptography that enables a party to
selectively receive one of several pieces of information from another party.
The sender remains unaware of the recipient’s selection, ensuring privacy. It
is foundational to secure multi-party computations, playing a vital role in
maintaining secrecy and trust in data exchange [75].

A digital envelope is a cryptography concept that involves the use of two
keys: a public key to encrypt data, and a private key to decrypt it. The term
”digital envelope” is derived from the analogy of placing a sealed message
(encrypted data) in an envelope for secure transmission.

Secure multiparty computation (MPC) is a technique that enables multiple
parties to jointly compute a function over their private inputs without
revealing them to each other [67]. It allows the parties to securely
collaborate and compute a result that they would not be able to obtain
individually due to the privacy of their inputs.

The recent literature abounds with advancements in cryptographic methods,
demonstrating their potential to deliver privacy-preserving solutions in MaaS
applications. Luo et al. [62] constructed a privacy-preserving ride-matching
scheme (pRide) with a light homomorphic encryption between rider’s start point
and driver’s current location using road network distances and Yao’s garble
circuit. [63] presents P2Ride, a privacy-preserving ride-matching scheme for
ridesharing services that uses homomorphic encryption and secure multi-party
computation techniques to protect the privacy of passenger and driver
locations while matching them efficiently. The system is evaluated using real-
world datasets, and the authors claim that P2Ride provides a practical
solution that balances the efficiency and privacy concerns of both passengers
and drivers. The article also discusses the importance of privacy in
ridesharing services and highlights the potential privacy risks associated
with existing ride-matching approaches. [64] proposed a zone-based minimum
road travel time estimation technique with minimum travel time called PSride,
using ciphertext packing protocols to schedule taxis with minimum additional
travel time. Yu et al. [65] proposed an efficient exact shortest distance over
encrypted location for privacy-preserving in an online ride-hailing. Based on
SHE, [66] proposed an encrypted aggregate distance computation method to
calculate aggregate distances from a group of passengers to large-scale
drivers. [76] provides an user-privacy in ride-sharing to matches drivers with
riders, a filter protocol based on homomorphic arithmetic secret sharing and
secure two-party equality test to choose the drivers with whom the rider can
travel. In [67] the authors present a protocol for ensuring user privacy in
ridesharing services. The system utilizes secure multiparty computation
techniques to compute the optimal route and fare for each ride without
revealing sensitive user information. The proposed system allows for both
transferable and non-transferable services, and it is evaluated through
simulations and experiments to demonstrate its efficiency and effectiveness in
preserving user privacy. The protocol also uses techniques such as
pseudonymization, encryption, and anonymous authentication to protect user
privacy. [68] proposed a protocol that uses secure multiparty computation
techniques to protect the privacy of users while determining the optimal
meeting points for ridesharing. The protocol allows users to specify their
preferences for the meeting point, such as a convenient location or a secure
place, without revealing their exact location or identity. The proposed system
is evaluated through simulations and experiments, and the results show that it
can provide efficient and privacy-preserving meeting points for ridesharing
services. Fengwei et al. [69] proposed an lightweight dynamic spatial query
scheme named TRACE to protect the sensitive data for online ride-hailing
providers and users based on random masking technique and point-in-polygon
strategy. The scheme uses bilinear pairing for encrypting driver and rider
locations, i.e, the encrypted space division information based on a quadtree
structure and symmetric encryption which avoid disclosing business secrets and
causing economic costs, then stored in rider and driver side. After a rider is
paired with a driver. A secure communication channel is needed to negotiate a
specific pick-up location. The integrity and authenticity of a message are
validated using a digital signature. [70] employed techniques such as road
network embedding and homomorphic encryption to accurately and confidentially
calculate the shortest distances between users in a ride-hailing system. Their
approach was designed to be efficient and maintain the privacy of the users’
information. [71] use somewhat homomorphic encryption and proposed a privacy-
preserving cross-zone ride-matching scheme (CRide) for online ride-hailing
while achieving an acceptable accuracy of matching algoritm.

###  4.3 Differential Privacy

Differential privacy reduces the risk of privacy breaches by adding a
controlled amount of random noise to the data being analyzed. The idea is to
add enough noise so that individual data points in the dataset cannot be
linked to any specific individual with high confidence, while still allowing
useful information to be extracted from the data [77, 78, 79, 80].

An algorithm is called epsilon-differentially private if it satisfies the
definition of differential privacy with a parameter $\epsilon$ (epsilon). The
parameter $\epsilon$ controls the amount of noise that is added to the output
of the algorithm in order to protect privacy.

The definition of epsilon-differential privacy states that for any two
datasets $D$ and $D^{\prime}$, which differ by only one individual’s record,
and any output $S$ of the algorithm:

$Pr[S(D)\in A]\leq exp(\epsilon)*Pr[S(D^{\prime})\in A]$

where $Pr[S(D)\in A]$ is the probability that the output $S$ of the algorithm
on dataset D belongs to a set $A$, and $exp(\epsilon)$ is the privacy budget
or privacy parameter. This definition states that the probability of obtaining
a particular output from dataset $D$ is at most exp($\epsilon$) times the
probability of obtaining the same output from dataset $D^{\prime}$. This
ensures that the algorithm does not reveal any information about any
individual in the dataset with high probability.

In practice, the value of $\epsilon$ is chosen based on the sensitivity of the
data being analyzed and the desired level of privacy protection. A smaller
value of $\epsilon$ corresponds to stronger privacy protection but may result
in more noise being added to the output, which could reduce the accuracy of
the algorithm. Conversely, a larger value of $\epsilon$ corresponds to weaker
privacy protection but may result in less noise being added to the output,
which could improve the accuracy of the algorithm [81].

Differential privacy methods use various probability distributions to add
noise to queries and protect the privacy of sensitive data. Laplace
distribution is a popular choice due to its heavy tail, which allows it to add
more noise to extreme values. It is often used for queries with high
sensitivity and limited privacy budget. Exponential distribution models the
time between events in a Poisson process and is commonly used when the
sensitivity of the query is unknown. Gaussian distribution is a bell-shaped
distribution and is preferred when the sensitivity of the query is relatively
small and a smaller amount of noise is required to maintain accuracy. The
choice of distribution depends on the specific application and privacy
requirements [82].

In the realm of applying differential privacy to ride-hailing and ride-sharing
platforms, several approaches have been proposed, each with unique
methodologies. For instance, Xu et al. [77] proposed a geo-
indistinguishability-based framework to perturb location in an online minimum
bipartite matching problem and preserve the privacy of individuals on ride-
sharing platforms while minimizing the total waiting time of passengers. The
riders send perturbed location to an untrusted third-party platform. Then,
this later performs an online assignment algorithm and establishes a direct
connection with the requester. The techniques used in [80] include
differential privacy and pseudonymization of the user’s location data. The
authors also suggest the use of a trusted third party to ensure the anonymity
of the user’s identity. [79] use differential privacy techniques to protect
user privacy while collecting and analyzing data on user demand and mobility
patterns for the optimization of fleet management. The system uses a
hierarchical data aggregation technique to collect data at various levels of
aggregation while ensuring privacy. The authors also propose a privacy-
preserving algorithm for the optimization of fleet management that uses the
collected data. [78] and [83] proposes a privacy-preserving framework for
ride-hailing services that employs several privacy-preserving techniques,
including location perturbation, randomized service selection, trajectory
blurring, and differential privacy. These techniques work in unison to protect
the user’s location data and travel patterns while still enabling the service
to deliver precise location-based services. Location perturbation obscures the
user’s actual location, randomized service selection limits the amount of
information service providers can access, trajectory blurring obscures the
user’s travel path, and differential privacy adds an extra layer of protection
for the user’s location data. Together, these techniques ensure that ride-
hailing service users’ privacy is safeguarded without compromising the quality
of the services provided.

###  4.4 Distributed learning

Distributed learning for data privacy refers to the use of distributed machine
learning techniques to train models using data from multiple sources while
preserving the privacy of each data source. Federated learning and split
learning are two popular approaches to distributed learning for data privacy
in MaaS [84].

Federated learning is a technique where multiple devices or edge servers train
a shared model collaboratively without sharing raw data with a central server
[85]. Instead, each device performs local training on its own data, and only
model updates are sent to the central server. The central server aggregates
the updates and sends the updated model back to the devices, and the process
repeats until convergence. Federated learning enables privacy preservation
because raw data never leaves the devices, and the model updates are encrypted
during transmission [72]. The main drawback of federated learning is the need
for a high-bandwidth and low-latency communication infrastructure to transmit
the model updates efficiently. It can also suffer from communication overhead
if the model is large or the number of devices is significant. Additionally,
federated learning assumes that each device has an adequate amount of data to
train on, which may not always be the case.

Split learning, on the other hand, is a technique where the model is split
between a central server and multiple edge devices. Each device performs local
training on a portion of the model and sends only the updated weights to the
central server. The central server aggregates the updates and updates the
model accordingly [86]. Split learning also enables privacy preservation
because the raw data never leaves the edge devices, and only model weights are
transmitted. Split learning requires a more complex model architecture, which
may increase the computational cost of training. Additionally, split learning
may not perform well when the edge devices have limited computing power or
low-quality data. Furthermore, the edge devices may require frequent updates
to their local models, which can increase the communication overhead.

[87] introduces Fed-ltd, a cross-platform ride-hailing approach that uses
federated learning to train a dispatching model while preserving user data
privacy. The method partitions user data across platforms and trains a local
model on each platform, which are then aggregated to create a global model.
Real-world datasets are used to evaluate the approach, demonstrating superior
performance compared to traditional methods.

###  4.5 Blockchain

A blockchain is a tamper-proof, transparent, decentralized and distributed
ledger that stores transactions using cryptographic hashes. It prevents fraud
and diminishes security threats. It consists of a chain of blocks where each
block includes several immutable transactions inside the blockchain network. A
block encompasses various information in two sections: The header (the block
index, Merkle root, timestamp, block hash of the previous block, and the data
themselves), the body that store the transaction details. The first block is
called the genesis block or zero block which preserves the ownership of the
transactions. Blockchain requires a public–private key pair for the validation
of the data. Moreover, there are three types of blockchains, namely public
(permissionless), private (permissioned controlled by the specific
enterprise), and consortium (federated blockchain, similar to a private
blockchain). Transaction in blockchain is managed using smart contracts under
concrete conditions. In addition, BT encompasses several consensus algorithms,
for instance, Proof-of-Work (PoW), Proof-of-Activity (PoA), Proof-of-State
(PoS), Practical Byzantine Fault Tolerance (PBFT), Delegated PoS (DPoS), Proof
of-Burn (PoB), Federated BFT (FBFT), and Proof-of-Elapsed Time (PoET), to
ensure reliability within the network. The decentralized nature of the
blockchain means that it operates on a peer-to-peer network, eliminating the
need for intermediaries to verify and validate transactions. This makes it an
ideal technology for various applications, including digital currencies,
supply chain management, and identity management [88], [89].

Blockchain technology has the potential to enhance MaaS by improving the
efficiency, security, and transparency of transportation and mobility systems
[90].

One of the main benefits of using blockchain in MaaS is the ability to
securely and transparently store and track data related to mobility services,
such as vehicle usage, payment information, and trip history. This allows for
more efficient and effective management of mobility services, as well as
improved trust and accountability between the various parties involved. The
decentralized nature of blockchain can also help to eliminate intermediaries,
reduce transaction costs, and increase the speed of transactions. This can
make it easier and more cost-effective for users to access and use various
mobility services, such as car-sharing, ride-hailing, and public
transportation. Furthermore, the use of smart contracts can help to automate
many of the processes involved in MaaS, such as payments, scheduling, and
route planning. This can improve the overall efficiency and reliability of the
mobility services, as well as increase user satisfaction.

[91] primarily focuses on the use of blockchain technology to enable secure
and decentralized record-keeping of ride-hailing transactions. The consortium
blockchain-based system ensures that data related to ride-hailing transactions
is stored in a tamper-proof and decentralized manner, reducing the risk of
unauthorized access or manipulation of the data. Additionally, the paper
proposes the use of vehicular fog computing, which involves using the
computing resources of nearby vehicles to process and analyze data related to
ride-hailing transactions. [92] propose a permissioned blockchain-based
approach for enhancing the security and privacy of user data in Mobility-as-a-
Service (MaaS) transactions, which include ride-hailing, public
transportation, and other transportation services. This is achieved through
the use of blockchain technology, which enables secure and decentralized
record-keeping of MaaS transactions, making it tamper-proof, secure and
eliminating the need for commercial agreements between the various MaaS
agents.. Additionally, the paper suggests the use of smart contracts, which
are self-executing contracts that allow for automated and secure data sharing
between different MaaS service providers, while still maintaining the privacy
of user data. [93] adresses the evolution of ride-sharing services from
centralized models to decentralized ones. The article discusses how
decentralized ride-sharing services can enhance user privacy by reducing the
amount of personal data shared with centralized authorities. In [94], a
blockchain for real-time ride-sharing is presented with low communication and
computation overheads. The ride share area is presented as multiple
overlapping grids. The privacy of matching is achieved through encryption of
offers and requests. The system employs encryption techniques to protect the
sensitive data of users, such as their location and identity. Specifically,
the system uses homomorphic encryption to encrypt the location of users in a
way that enables ride matching without disclosing the exact location of users.
The system also uses zero-knowledge proofs to enable users to prove their
identity and eligibility to use the system without revealing their identity or
personal information. [88], [89] proposed a method to protect the privacy of
ride-sharing participants using a public Blockchain system named B-Ride. The
location of the rider is concealed through cloaking, a technique that divides
the coverage area into large cells. The rider’s location privacy is maintained
by disclosing only the cell number of the driver or rider, rather than their
exact location. Ride requests are uploaded to the Blockchain, and drivers
locally match them before sending encrypted ride offers to the matched riders’
public keys. However, the use of cloaking in B-Ride results in a privacy-
accuracy tradeoff. While larger cells improve privacy, accuracy suffers as two
distant users can be considered a match. In addition, B-Ride does not protect
the driver’s privacy. [95] utilizes a consortium blockchain to protect ride-
sharing services. To increase both transparency and security, an attribute-
based proxy reencryption algorithm has been implemented within the system.
[96] proposes a blockchain-based data pricing scheme called Block-CPS for car
sharing over a 5G network, which is secure and efficient. An IPFS-based data
storage system ensures secure and cost-efficient data transactions within
Block-CPS. The article uses a non-cooperative game-theoretic approach to
achieve optimal pricing for both vehicle owners and customers. Blockchain
integration further enhances security. [97] proposes a ride-hailing service
that uses private smart contracts to provide anonymous and secure service. The
smart contract mechanism ensures that user and driver identities are kept
private and only authorized parties can access ride information. Payment is
only made after the completion of the ride, and a reputation system
incentivizes good behavior. The paper includes a prototype implementation and
experimental evaluation to demonstrate the feasibility of the proposed system.

###  4.6 Gaps and limitations in the current literature

Table 2 provides a summary of the key aspects covered in the studied papers
organized by publication year, and various data privacy techniques and
technologies relevant to MaaS. The goal of the Table 2 is to provide a quick
overview of the current literature on data privacy in MaaS, showing which
papers have covered which MaaS services and privacy techniques. This allows
for easy identification of the coverage and gaps in the existing literature.

Based on the current literature, several gaps and limitations can be
identified in the area of data privacy within MaaS. Addressing these gaps and
limitations can contribute to a more comprehensive understanding of MaaS data
privacy and help in the development of more effective privacy-preserving
solutions :

  * 1.

Lack of comprehensive coverage: There is no single paper that extensively
covers all MaaS services, such as ridesharing, ride-hailing an other services,
with a specific focus on data privacy. This suggests the need for more
research that holistically addresses privacy challenges and requirements
across various MaaS services.

  * 2.

Insufficient focus on emerging technologies: The existing literature may not
sufficiently address the role of emerging technologies, such as edge computing
in enhancing data privacy in MaaS. Further research is needed to explore the
potential of these technologies to improve privacy-preserving solutions in
MaaS.

  * 3.

Limited exploration of interdisciplinary approaches: Many papers focus on
specific data privacy techniques, while a more interdisciplinary approach that
combines multiple techniques may lead to more robust privacy-preserving
solutions. Research that investigates the integration of various methods, such
as anonymization, cryptography, and differential privacy, could yield more
comprehensive results.

  * 4.

Inadequate attention to regulatory compliance: Although some papers discuss
legal and regulatory aspects of data privacy in MaaS, there is still a need
for more research that analyzes the implications of local and international
data protection laws and how MaaS providers can ensure compliance.

  * 5.

Narrow evaluation of privacy techniques: Current literature may not provide a
thorough evaluation of the effectiveness of different privacy techniques in
MaaS settings. Future research should include more extensive empirical studies
and comparisons of privacy-preserving methods, taking into account various
performance metrics and real-world scenarios.

  * 6.

Insufficient focus on user-centered design: Research on data privacy in MaaS
could benefit from a stronger emphasis on user-centered design and user
privacy preferences. Investigating user perceptions and expectations related
to privacy in MaaS can help to design more effective and user-friendly
privacy-preserving solutions.

Table 2: Relevant papers

Paper |  Year |  Ride-sharing |  Ride-hailing |  Edge computing |  Blockchain |  Distributed learning |  Anonymization |  Cryptography |  Differential privacy
---|---|---|---|---|---|---|---|---|---
[76] |  2018 |  ✓ |  – |  – |  ✓ |  – |  ✓ |  ✓ |  –
[55] |  2022 |  |  ✓ |  – |  – |  – |  – |  ✓ |  –
[88] |  2020 |  ✓ |  – |  – |  ✓ |  – |  ✓ |  ✓ |  –
[89] |  2021 |  ✓ |  – |  – |  ✓ |  – |  ✓ |  ✓ |  –
[95] |  2021 |  ✓ |  – |  – |  ✓ |  – |  – |  ✓ |  –
[78] |  2018 |  – |  ✓ |  – |  – |  – |  ✓ |  – |  –
[83] |  2020 |  – |  ✓ |  – |  – |  – |  ✓ |  – |  –
[54] |  2021 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  ✓
[66] |  2020 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[64] |  2021 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[98] |  2019 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[62] |  2019 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[99] |  2019 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[61] |  2019 |  – |  ✓ |  – |  – |  – |  ✓ |  – |  –
[65] |  2022 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[68] |  2016 |  ✓ |  – |  – |  – |  – |  – |  ✓ |  –
[53] |  2018 |  – |  ✓ |  – |  – |  – |  – |  – |  ✓
[42] |  2018 |  – |  ✓ |  – |  – |  – |  – |  – |  ✓
[71] |  2022 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[97] |  2022 |  – |  ✓ |  – |  ✓ |  – |  – |  ✓ |  –
[100] |  2020 |  ✓ |  – |  – |  – |  – |  ✓ |  – |  –
[67] |  2021 |  ✓ |  – |  – |  – |  – |  – |  ✓ |  –
[69] |  2018 |  – |  ✓ |  – |  – |  – |  – |  ✓ |  –
[77] |  2020 |  ✓ |  – |  – |  – |  – |  – |  ✓ |  –
[80] |  2021 |  – |  ✓ |  ✓ |  – |  – |  ✓ |  ✓ |  –
[91] |  2019 |  – |  ✓ |  ✓ |  ✓ |  – |  – |  ✓ |  –
[77] |  2020 |  ✓ |  – |  – |  – |  – |  – |  – |  ✓
[94] |  2021 |  ✓ |  – |  – |  ✓ |  – |  ✓ |  ✓ |  –
[63] |  2022 |  ✓ |  – |  – |  – |  – |  ✓ |  ✓ |  –
[96] |  2022 |  ✓ |  ✓ |  – |  ✓ |  – |  – |  ✓ |  –
[87] |  2022 |  – |  ✓ |  – |  – |  ✓ |  – |  – |  –

##  5 Future Research Directions

As MaaS data privacy continues to evolve, promising research directions are
emerging. In this section, we will presents three possible areas of
exploration: game theory, edge computing, and evaluation of data privacy
methods in MaaS. The ongoing exploration of these research directions will
contribute to design more robust and context-aware privacy-preserving
mechanisms.

Game theory can be a useful tool in the context of data privacy as it can
analyze the incentives with respect to the collection and use of personal
data, as well as the trade-off between privacy and convenience. It can also be
used to analyze the strategies that individuals and organizations adopt to
protect their privacy and control their personal data. Deng et al. propose a
new framework for designing incentives and profit-sharing mechanisms in multi-
modal transportation networks that encourage efficient and sustainable
transportation solutions [101]. Zheng et al. propose a method for mobile
location privacy access control using game theory to address privacy concerns
that arise from the use of LBS [26]. This method sets weight coefficients for
location privacy factors, calculates an access control threshold based on
mobile location privacy leakage, and determines an access control strategy
based on a comparison with the threshold and a selection from a strategy
matrix.

Edge computing can also enhance data privacy in MaaS in several ways. Firstly,
it allows for local data processing and aggregation, reducing the risk of data
breaches and protecting individual user privacy [102, 80]. Second, it supports
federated learning, enabling data analysis without transmitting data to a
central server. Third, it provides trusted execution environments, ensuring
secure processing of sensitive data. Fourth, it allows for user-controlled
data storage and processing, reducing the amount of data transmitted and
stored centrally. Additionally, edge computing provides real-time processing,
improving efficiency and enhancing the user experience by enabling the
implementation of sophisticated algorithms that can predict ride demand,
optimize routes, and manage traffic [102]. Future research can explore trade-
offs between privacy, latency, and resource consumption in edge computing
scenarios, leading to more efficient and scalable privacy-preserving
solutions.

Comparing data privacy methods in MaaS can be challenging due to several
factors. The lack of standardization makes it difficult to measure or evaluate
privacy, leading to challenges in comparing different methods and establishing
best practices. Privacy methods and techniques are context-dependent, meaning
that what works well in one context may not be appropriate for another.
Privacy measures often involve trade-offs between privacy protection and other
factors such as usability, functionality, and cost. The data privacy landscape
is constantly evolving, making it difficult to evaluate the effectiveness of
privacy methods over time and keep up with the latest developments. Finally,
limited data availability on privacy breaches or violations makes it
challenging to assess the effectiveness of different privacy methods.
Nevertheless, several methods can be used in MaaS, such as PIA, which can
systematically evaluate potential privacy risks and impacts. Privacy metrics,
such as the number of data breaches or requests for data deletion, can also be
developed. User feedback can be collected through surveys or interviews to
identify areas of concern and improvement. External auditing can assess
compliance with regulations, and internal monitoring, such as access log
monitoring and security audits, can ensure that data privacy measures are
effective [103].

##  6 Conclusions

Data privacy techniques are crucial in MaaS to protect users’ sensitive data
from privacy risks. MaaS manipulate a wide range of data types, including
location data, personal information, health information, and biometric data,
which must be handled with care to prevent malicious activities such as
cybercrime, surveillance and discrimination.

Laws and regulations play an essential role in protecting users’ privacy in
MaaS, and MaaS applications should comply with these regulations to ensure
that users’ data is used ethically and transparently. The GDPR and CCPA are
examples of regulations that have been enacted to ensure that users’ privacy
is protected.

However, implementing data privacy techniques in MaaS poses significant
challenges, including the need to balance privacy and convenience, the
complexity of data flows, and the need for interoperability and
standardization across different systems.

Privacy techniques such as anonymization, cryptography, differential privacy,
distributed learning methods and blockchain are emerging, which can enhance
data privacy in MaaS while preserving data utility. Additionally, the use of
decentralized and distributed architectures could enhance privacy and security
while reducing the need for centralized data management, leading to more
widespread adoption and usage of these services.

Looking towards the future, there is a need for further research in data
privacy for MaaS. Game theory can be utilized to identify privacy risks and
design privacy-preserving mechanisms. Edge computing is a more secure and
private way of processing and storing data, as it reduces the amount of data
transmitted to centralized servers. Evaluating different privacy-enhancing
technologies is necessary to determine their effectiveness in protecting user
privacy while ensuring data quality and service efficiency. Advancing our
understanding of data privacy in MaaS through research will lead to more
trustworthy and secure systems, benefiting both users and service providers.

##  7 Declaration of Generative AI and AIassisted technologies in the writing
process

During the preparation of this work, the authors utilized ChatGPT by OpenAI in
order to assist with content correction and linguistic refinement. After using
this tool, the authors reviewed and edited the content as necessary and take
full responsibility for the content of the publication.

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