# Location Privacy in B5G/6G: Systematization of Knowledge

Hannah B. Pasandi  University of California - BerkeleyBerkeleyCAUSA
<EMAIL_ADDRESS>and  Faith Parastar  Independent Researcher

###### Abstract.

As we transition into the era of B5G/6G networks, the promise of seamless,
high-speed connectivity brings unprecedented opportunities and challenges.
Among the most critical concerns is the preservation of location privacy,
given the enhanced precision and pervasive connectivity of these advanced
networks. This paper systematically reviews the state of knowledge on location
privacy in B5G/6G networks, highlighting the architectural advancements and
infrastructural complexities that contribute to increased privacy risks. The
urgency of studying these technologies is underscored by the rapid adoption of
B5G/6G and the growing sophistication of location tracking methods. We
evaluate current and emerging privacy-preserving mechanisms, exploring the
implications of sophisticated tracking methods and the challenges posed by the
complex network infrastructures. Our findings reveal the effectiveness of
various mitigation strategies and emphasize the important role of physical
layer security. Additionally, we propose innovative approaches, including
decentralized authentication systems and the potential of satellite
communications, to enhance location privacy. By addressing these challenges,
this paper provides a comprehensive perspective on preserving user privacy in
the rapidly evolving landscape of modern communication networks.

††isbn:

##  1\. Introduction

We are transitioning into a connected and intelligent era with the
commercialization of 5G and the ongoing standardization of 6G. These next-
generation cellular networks represent not just incremental updates, but a
profound technological revolution. They promise seamless connectivity with
significantly higher data rates, expanded coverage, and support for a wide
array of innovative applications such as Augmented/Virtual Reality (AR/VR),
smart health, smart transportation, the Internet of Things (IoT), and the
Industrial Internet of Things (IIoT) (ferrag2018security, ;
rafique2023securemed, ). Furthermore, 6G anticipates incorporating satellite
communication integration, Artificial Intelligence (AI) at the network edge,
Green network with more efficiency of energy consumption, and data
transmission at terabytes per second by using a heterogeneous access network.
Figure. 1illustrates a comprehensive list of services and applications. This
transformative potential stems from various groundbreaking technologies
deployed in these networks (dao2021survey, ; sandeepa2022survey, ).

The importance of studying B5G/6G lies in the need to understand and address
the complex privacy concerns associated with the enhanced precision and
ubiquitous connectivity of B5G/6G. The urgency is driven by the rapid
deployment of these networks and the growing capabilities of adversaries to
exploit location data, which can lead to severe privacy breaches and security
threats. This study provides a comprehensive analysis of the current state of
location privacy, identifies emerging threats, and evaluates the effectiveness
of existing and novel privacy-preserving mechanisms. By doing so, it lays the
groundwork for future research and development efforts aimed at ensuring user
privacy in the context of advanced communication networks. The insights gained
from this study will help inform the design of robust privacy frameworks,
guide policymakers, and enhance the security and trustworthiness of next-
generation networks. Given the critical nature of location data and the
potential for its misuse, it is imperative to focus on this aspect to protect
user privacy and maintain trust in emerging technologies.

Figure 1. B5G/6G Services and Applications

In 5G, key technologies such as Network Function Virtualization (NFV) and
Software Defined Networking (SDN) play important roles in reshaping network
architecture and operations. NFV enables the virtualization of network
functions, allowing them to run on standard hardware, thus enhancing
scalability and flexibility. SDN, on the other hand, decouples the control
plane from the data plane, enabling centralized network management and dynamic
resource allocation. Multiple Input Multiple Output (MIMO) technology in 5G
significantly enhances spectral efficiency and network capacity by utilizing
multiple antennas for transmission and reception, thereby enabling higher data
rates and improved coverage (schmitt2021pretty, ; xie2022ng, ;
porambage2021roadmap, ; nayak20216g, ; rahman2022deep, ). Looking ahead to B5G
and 6G, further advancements are anticipated, leveraging technologies such as
terahertz frequencies, intelligent surfaces, and quantum communication.
Terahertz frequencies offer the potential for ultra-high data rates and
extremely low latency, enabling a new generation of applications. Intelligent
surfaces, also known as reconfigurable intelligent surfaces (RIS), manipulate
electromagnetic waves to enhance wireless communication, improve coverage, and
mitigate interference. Quantum communication, leveraging the principles of
quantum mechanics, promises unparalleled security and ultra-fast data
transmission rates, revolutionizing the landscape of secure communication in
B5G/6G networks (iavich2021novel, ; norrman2016protecting, ).

Despite the technological advancements and the AI (1, ; 2, ; 3, ; 4, ; 5, ; 6,
) role in 5G and beyond era (yang2022federated, ), concerns regarding privacy
and security persist, particularly in the context of location data. For
example, the integration of FL and AI also expands the vulnerability surface.
The decentralized nature of FL introduces potential attack vectors such as
model poisoning, where malicious entities could corrupt the learning process.
Similarly, AI systems, despite their robustness, can be susceptible to
adversarial attacks that exploit weaknesses in their training data or
algorithms. As B5G/6G networks become more reliant on these technologies, it
is crucial to develop advanced security measures to mitigate these risks and
ensure the integrity and reliability of the network (lin2024split, ).

Therefore, the initial privacy issues in B5G/6G networks arise from the
fundamental architectural differences (Section 3) introduced in their design.
Unlike their predecessors, these advanced networks incorporate decentralized
architectures, heterogeneous network components, and advanced technologies
like Massive MIMO, mmWave communications, and edge computing. These
infrastructural complexities significantly increase the precision and volume
of location data collected, thus amplifying the risk of privacy breaches.
Consequently, the increased granularity and accuracy of location tracking in
B5G/6G networks expose user data to sophisticated tracking methods and
unauthorized access, highlighting the urgent need for robust privacy-
preserving mechanisms to protect sensitive information (Section 4).

To provide a better understanding of the current and emerging 5/6G
technologies with respect to privacy concerns and mitigation, this paper
delves into the intricate landscape of user privacy with the focus on location
privacy in B5G/6G networks, exploring the challenges and proposed solutions in
protecting user privacy in the evolving technological landscape. Through a
systematization of knowledge, we shed light on the complexities of this
critical issue and provide insights for future research and development
efforts (sandeepa2022survey, ).

Contributions. This paper makes the following key contributions to the study
of architectural and infrastructural complexities, and location privacy in
B5G/6G networks:

  * •

Comprehensive Analysis of Location Privacy Challenges. We provide an in-depth
analysis of the unique location privacy challenges posed by B5G/6G networks.
This includes the infrastructural complexities, increased data precision, and
the evolving threat landscape that significantly impact user privacy.

  * •

Evaluation of Privacy-Preserving Mechanisms We systematically review and
evaluate existing and emerging privacy-preserving mechanisms. This evaluation
covers techniques such as anonymization, pseudonymization, and homomorphic
encryption, assessing their effectiveness in the context of B5G/6G.

  * •

Architectural and Infrastructural Complexities: We explore the architectural
and infrastructural complexities introduced by B5G/6G networks, such as the
transition from centralized to distributed architectures, the integration of
heterogeneous network components, and the deployment of advanced technologies
like Massive MIMO, mmWave communications, and edge computing. These
complexities are analyzed in terms of their implications for network
performance, scalability, and location privacy.

  * •

Innovative Solutions for Enhanced Privacy: We propose novel approaches to
enhance location privacy, including decentralized authentication systems and
the integration of satellite communications. These solutions aim to address
the identified privacy challenges and provide robust protection against
unauthorized tracking and data breaches.

  * •

Guidance for Future Research and Policy Development: By identifying the gaps
and limitations in current privacy-preserving strategies, this paper offers
valuable insights for future research directions and policy development. We
emphasize the need for regulatory oversight and user awareness to create a
comprehensive framework for protecting location privacy in next-generation
networks.

Figure 2. Cellular Network Evolution

##  2\. Mobile Network Evolution

The transition from 4G to 5G and beyond (Figure. 2) marks a significant leap
in mobile network technology, bringing substantial enhancements in
connectivity and speed alongside new challenges in location privacy. While 4G
networks enabled applications requiring moderate location accuracy, such as
navigation and social media, 5G networks offer much more precise location
tracking. This improved precision, achieved through higher frequency bands
like millimeter-wave (mmWave) and advanced positioning techniques, is
essential for applications such as AR, smart cities, and autonomous vehicles.
However, this increased accuracy also raises significant privacy concerns, as
more detailed location data becomes available and potentially exploitable.

The technological advancements in 5G, including Software Defined Networking
(SDN), Network Function Virtualization (NFV), and edge computing, contribute
to the precision of location services while introducing new privacy
challenges. SDN and NFV enable flexible and efficient management of network
resources, crucial for maintaining high accuracy in dynamic environments. Edge
computing processes data closer to the user, reducing latency and enhancing
real-time location data processing. This local processing capability is
critical for applications requiring instant location updates, such as
emergency services and drone navigation. However, the increased granularity of
location data processed at the edge also amplifies the risks of unauthorized
access and privacy breaches, necessitating robust privacy-preserving
mechanisms.

B5G/6G networks are set to enhance location capabilities further with advanced
technologies, while simultaneously posing greater privacy risks. B5G aims to
refine the precision and reliability of 5G, while 6G will integrate artificial
intelligence (AI) and machine learning (ML) to provide unprecedented levels of
location accuracy and context-awareness. Innovations such as terahertz (THz)
frequencies, intelligent surfaces, and quantum communication are expected to
significantly improve location tracking. These advancements will enable ultra-
high-definition location services for applications like remote surgery and
immersive virtual environments. However, the ability to track user locations
with such high precision increases the potential for privacy infringements,
making it essential to develop and implement advanced privacy-preserving
technologies.

As mobile networks evolve from 4G to B5G and 6G, the complexity of managing
and securing location data intensifies, highlighting the urgent need for
robust privacy measures. The deployment of new technologies requires extensive
infrastructure upgrades and introduces new challenges in data privacy and
security. Higher frequency bands, such as mmWave and THz, necessitate a denser
network of small cells, complicating the network architecture and increasing
the potential for location data breaches. The enhanced capability to track
user locations with high precision exacerbates privacy concerns, necessitating
advanced security protocols and privacy-preserving techniques. The integration
of AI and ML in 6G networks, while improving location accuracy, adds layers of
complexity in ensuring that location data is handled securely and ethically,
emphasizing the need for comprehensive privacy frameworks.

Figure 3. Architectural Evolution of Cellular Networks

##  3\. Architectural Differences and Implications

The architectural differences between successive generations of cellular
networks, such as 4G, 5G, and upcoming B5G/6G, have profound implications for
network performance, scalability, and security. These differences stem from
advancements in technology, changes in network architecture, and evolving user
demands (Figure. 3). One notable architectural shift is the transition from
centralized to distributed architectures. In 4G networks, core network
functions are typically centralized, leading to potential bottlenecks and
scalability challenges. In 4G networks, localization and positioning methods
primarily rely on a combination of cellular network infrastructure, satellite
systems, and Wi-Fi access points. With methods such as cell-ID based
positioning, WiFi positioning system (WPS), Assisted-GPS (A-GPS), 4G network
could locate users with accuracy of 10m and 1km (del2017survey, ;
checa2020location, ; becker2022could, ).

In contrast, 5G networks embrace a more distributed architecture, leveraging
concepts like NFV and SDN to decentralize network functions. the NGC decouples
the user plane (or data plane) from the control plane (CP). This function,
which is known as CUPS2 (Control & User Plane Separation), was first
introduced in 3GPP release 14. An important characteristic of this function
being that, in case of a traffic peak, you can dynamically scale the CP
functions without affecting the user plane operations, allowing deployment of
UP functions (UPF) closer to the RAN and UE to support use cases like Ultra
Reliable low latency Communication (URLLC). This decentralization improves
scalability, flexibility, and reliability, enabling more efficient resource
allocation and dynamic network management. Another significant architectural
difference lies in the increased deployment of small cells and the utilization
of mmWave frequencies in 5G and beyond. Small cells, including enhanced
network capacity and coverage, particularly in dense urban environments.
mmWave frequencies enable higher data rates and lower latency but require
denser deployment due to shorter propagation distances. This architectural
shift necessitates new infrastructure planning and deployment strategies to
optimize coverage and capacity while mitigating interference and propagation
challenges. However, the localization accuracy with mmWave transmissions can
be on the order of 10 m or less outdoors and 3 m indoors, in part thanks to
cooperative gNBs and massive-MIMO with angle-of-arrival (AoA) and angle-of-
departure measurements (del2018whitepaper, ). Figure 3 illustrates the
architectural evolution of cellular networks, from 2G to 5G.

Edge computing represents a paradigm shift in network architecture, bringing
computation and data storage closer to the network edge, but at the same time
causing more concise location data. By processing data locally at the edge of
the network, edge computing reduces latency, enhances real-time
responsiveness, and improves scalability. This architectural evolution is
particularly relevant for latency-sensitive applications such as AR/VR and
autonomous vehicles, where real-time data processing is critical.

Network slicing is a key architectural concept in 5G and beyond, enabling the
creation of virtualized network instances tailored to specific use cases or
service requirements. By dynamically allocating network resources based on
application demands, network slicing enables efficient resource utilization,
improved QoS, and enhanced network customization. However, implementing
network slicing requires robust orchestration and management frameworks to
ensure isolation, security, and performance guarantees for each slice.
Moreover, the proliferation of network slices adds complexity to network
operations and monitoring, necessitating sophisticated management and
automation solutions. Figure. 5 shows different levels of network slicing.

the deployment of small cells and the integration of edge computing introduce
new opportunities for location-based services and applications. However, they
also raise concerns about the collection, processing, and sharing of location
data. Small cells, being deployed in closer proximity to users, enable more
precise localization and tracking, which can compromise user privacy if not
properly regulated. Additionally, edge computing platforms may aggregate and
analyze location data from multiple sources, creating potential privacy risks
if sensitive information is exposed or misused (tomasin2021location, ).

The 6G network core is anticipated to build upon the foundation established by
5G but with significant enhancements and new features to meet the evolving
demands of future communication technologies, such as integrated AI/ML and
Sustainability and Energy Efficiency considerations. with seamless integration
of terrestrial networks with non-terrestrial networks such as satellites,
HAPs, and UAVs enhances the accuracy and availability of location data. This
integration ensures continuous and precise location tracking even in remote
areas where terrestrial infrastructure is sparse. The fusion of multiple
sources of location data from different types of networks improves overall
localization accuracy and reliability. Further Enhanced satellite
communication provides global positioning capabilities with higher precision,
enabling accurate location tracking anywhere on the planet. This is
particularly useful for applications requiring precise geolocation, such as
navigation in remote areas, environmental monitoring, and global logistics
(ferrag2018security, ; rafique2023securemed, ; sandeepa2022survey, ).

##  4\. Location Privacy & It’s challenges in B5G/6G Networks

Location data privacy is paramount in today’s digital age due to its pervasive
nature and potential for misuse. With the advent of advanced mobile
technologies like 5G and the imminent arrival of B5G/6G networks, the
granularity and accuracy of location tracking have reached unprecedented
levels. Every time an individual accesses the internet, makes a call, or uses
location-based services, their precise whereabouts are logged, creating a
digital footprint that can reveal sensitive information about their daily
routines, habits, and even personal preferences. This wealth of location data
is highly coveted by various stakeholders, including advertisers, marketers,
and even malicious actors seeking to exploit it for financial gain or
nefarious purposes. Misuse of location data poses severe risks, such as
unauthorized tracking and profiling, which can lead to stalking, identity
theft, and physical harm. These dangers underscore the critical need for
robust privacy protection measures to protect individuals’ sensitive
information and prevent malicious exploitation (jiang2021location, ).

Despite the proliferation of privacy-preserving techniques and regulatory
frameworks aimed at protecting user data, the privacy of location remains a
persistent challenge. One key reason for this ongoing struggle is the inherent
tension between privacy and utility in the digital ecosystem. While users
demand seamless and personalized services that leverage their location data,
they also expect robust privacy protections to prevent unauthorized access or
misuse. Balancing these competing interests is no easy feat, as evidenced by
the numerous instances of data breaches, privacy violations, and regulatory
lapses that continue to plague the industry. Moreover, the rapid pace of
technological innovation and the ever-expanding array of interconnected
devices further complicate efforts to maintain privacy in an increasingly
interconnected world (haddad2023blockchain, ; babaghayou2023safety, ).

Furthermore, the evolving threat landscape and the emergence of novel privacy
risks underscore the need for continuous improvement in location data privacy
measures. Despite the existence of encryption protocols, anonymization
techniques, and data minimization practices, vulnerabilities persist, leaving
users vulnerable to exploitation and manipulation. Moreover, the proliferation
of IoT devices, smart sensors, and wearables only exacerbates these challenges
by introducing new vectors for data collection and analysis. As such,
addressing the privacy implications of location data requires a multifaceted
approach that combines technical innovation, regulatory oversight, and user
education to create a more resilient and privacy-centric digital ecosystem
(sandeepa2022survey, ; khan2020survey, ; wang2024gees, ).

Maintaining user privacy is challenging in cellular networks, both past and
present as it is not a primary goal of the architecture. In order to
authenticate users for access and billing purposes, networks use globally
unique client identifiers. Like- wise, the cellular infrastructure itself must
always “know” the location of a user in order to minimize latency when
providing connectivity (schmitt2021pretty, ). New advancements of B5G/6G
provide us with seamless- high speed connection, new services and applications
with unimaginable quality; But, they also have a dark side. These benefits
come with the price of possible privacy breaches. In following, we mentioned
some outstanding and beneficial features of B5G/6G that affect our privacy
more than before.

###  4.1. Increased Precision and Volume

With features such as mmWave, The integration of terrestrial and non-
terrestrial networks, including satellites and high-altitude platforms, B5G/6G
enhances global connectivity but also expands the scope of location tracking.
B5G/6G networks enable ultra-precise location tracking with accuracies down to
the decimeter level. While this is beneficial for applications like autonomous
vehicles and AR, it also raises concerns about intrusive surveillance and
unauthorized tracking. This high precision of location data increases the risk
of misuse by malicious actors, advertisers, or unauthorized third parties.
Detailed location information can be exploited for targeted advertising,
stalking, or profiling without the user’s consent. Also, Cross-border data
flows between terrestrial and non-terrestrial networks raise jurisdictional
and legal challenges for protecting location privacy. Different regulatory
frameworks and data protection laws across countries may result in gaps or
inconsistencies in privacy protections (anant2023introduction, ;
shaham2020privacy, ; lin2024single, ).

###  4.2. Advance Tracking Methods

As we mentioned in previous sections, cellular networks use GNS data to locate
the users. However with the advancements in B5G/6G this data are more accurate
and detailed. A-GPS enhances traditional GPS by using additional assistance
data from cellular networks to improve satellite acquisition and positioning
accuracy. It enables faster location fixes and works well in urban
environments or areas with obstructed GPS signals. WPS leverages WiFi signals
from nearby access points to determine the location of a device. By
triangulating signals from multiple WiFi access points, WPS can provide
accurate positioning, especially in indoor environments where GPS signals may
be weak or unavailable. Also, AI-driven analytics can analyze historical
location data to predict user behavior and preferences. While this enables
personalized services and recommendations, it also raises concerns about
predictive surveillance and intrusion into personal lives. As during the
corona virus pandemic lots of new tracking methods proposed based on ML
methods, while they were aiming health of society, they also could endanger
privacy of people (shubina2020survey, ; ciaparrone2020deep, ; bagayogo2024new,
).

###  4.3. Navigating the Complexity of B5G/6G Infrastructure

As we advance towards B5G and 6G networks, the infrastructural complexity of
cellular networks significantly increases, introducing new challenges and
opportunities for maintaining location privacy. The evolution from centralized
to decentralized architectures, the integration of heterogeneous network
components, and the incorporation of advanced technologies like Massive MIMO,
mmWave communications, and edge computing contribute to this complexity. These
enhancements aim to provide higher data rates, lower latency, and improved
connectivity, but they also introduce additional layers of vulnerability.
Ensuring robust location privacy in such a multifaceted and dynamic
environment requires a thorough understanding of these infrastructural
complexities and the development of sophisticated security measures to
mitigate associated risks. Some of main such complexities are:

####  4.3.1.  Multi Access Access Network.

Modern networks encompass a variety of access technologies such as cellular,
WiFi, Bluetooth, and satellite communication. Each of these technologies has
unique infrastructure and protocols for gathering location data, resulting in
fragmentation and challenges with interoperability. The access network in
B5G/6G is highly complex, incorporating terrestrial and non-terrestrial
elements such as drones, satellites, and connected vehicles to ensure
seamless, ubiquitous connectivity. This multi-layered infrastructure poses
significant challenges in terms of integration, management, and security,
necessitating advanced technologies and robust protocols to maintain efficient
and secure communication. Integrating location data from these diverse access
networks while maintaining consistency and accuracy is a significant
challenge, particularly when it comes to preserving privacy
(kazmi2023security, ; sandeepa2022survey, ; tomasin2021location, ;
schmitt2021pretty, ; lin2024single, ; wang2024gees, ). Figure 4 shows the
complexity of access network in B5G/6G.

Figure 4. B5G/6G Access Network

####  4.3.2. Edge and Fog Computing.

Mobile edge computing (MEC) is essential to the low-latency use cases that
form an integral part of B5G/6G. For example, organisers of a sports event may
want to provide slow motion videos or augmented reality content to people in
the stadium. This could be achieved by storing the content related to the game
in MEC servers closest to the stadium. Similarly, a factory may collect data
from sensors, vehicles, wearable devices and drones used in the factory and
process these locally helping it to operate more efficiently (hu2015mobile, ;
mourtzis2022design, ; liyanage2021driving, ). While the servers used in mobile
edge computing may reside closer to network elements to reduce latency, they
are essentially providing a cloud or hosting service. MEC not only decreases
latency but also lowers the energy required for data transmission, which is
one of important aspects of B5G/6G. Additionally, it can optimize resource
allocation and computational tasks dynamically, leading to more efficient use
of network and device power. The ability to process data in the network edge
in which it has been generated, could obviate the need to send the data
further into or outside the network, reducing wider circulation of personal
data than is necessary. It may be important to distinguish between the content
data which is stored locally by the MEC provider to provide the service and
the communications data which would remain subject to the usual licence
conditions and legal requirements (zhang2020edge, ; ranaweera2021survey, ).

By processing data closer to the user inherently precise location information
involved in optimizing performance and reducing latency. This proximity
increases the risk of location data being intercepted or misused, as the data
is handled at multiple edge nodes rather than centralized servers.
Additionally, the distributed nature of MEC creates more potential points of
vulnerability, making it harder to secure all nodes effectively. This
expansion of the attack surface heightens the potential for unauthorized
access and exploitation of sensitive location information (wang2024gees, ).

####  4.3.3. Network Slicing and Virtualization.

Network slicing enables the creation of multiple virtual networks on a shared
physical infrastructure, with each network customized for specific
applications or service needs. This architectural flexibility allows for
dynamic resource allocation and personalized network services, but it also
complicates the management and isolation of location data. To ensure privacy
in virtualized network environments, robust isolation mechanisms and strong
authentication protocols are necessary. To offer multiple heterogeneous
services from a single platform which is flexible in nature, the flexibility
is provided by a network slicing mechanism in which one physical network is
divided into multiple logical networks to offer these services simultaneously.

These slices use generic resources to serve users and specific applications.
SDN and NFV play a significant role in slicing to make the 5G network more
scalable and reliable. Network slicing deals with three prominent use cases:
the first enhances mobile broadband (eMBB) for those applications which
require high bandwidth to offer Ultra-HD video communication. This use case
increases the traffic load on mobile networks. The second massive machine type
communication (mMTC) offers connectivity between millions of devices. This use
case does not increase traffic but increases the magnitude of mobile networks.
The last service is URLLC which applies to connectivity, remote surgery,
industry 4.0, etc. in vehicles to everything (V2X). This use case needs very
low latency connectivity (dangi2022ml, ). The network slicing architectural
framework is based on three categories: RAN slicing, Core Network slicing and
End-to-End (E2E) slicing. Each network slice creation is done in four phases:
softwarization, virtualization, orchestration, and management.

Figure 5. B5G/6G Network Slicing

####  4.3.4. IoT and M2M communication.

The proliferation of IoT devices and machine-to-machine (M2M) communications
generates vast amounts of location data from sensors, actuators, and connected
devices. These devices often operate autonomously and may transmit location
information without explicit user consent. As these devices become
interconnected, they can communicate with nearby devices and stream data
continuously. This will create a highly sophisticated digital environment,
making it virtually possible to track every user action. Consequently, B5G/6G
networks will pave the way for a fully automated smart environment that could
encompass a vast population. Managing privacy in IoT deployments requires
implementing privacy-by-design principles, data minimization strategies, and
secure communication protocols (sandeepa2022survey, ). For the secure
collaboration between nodes and the detection of potential security threats
within the network, it is beneficial to establish trust rankings for nodes.
This is especially useful in defense and healthcare networks, where the
security of the network is heavily reliant on the trustworthiness of its nodes
(shafi2023intelligent, ).

####  4.3.5. Roaming.

One of the approaches that can help to the resilience of cellular networks is
roaming which facilitates mobile network operators to use the infrastructure
of each other when needed, e.g., in case its own infrastructure is not
functional or does not suffice to serve with the required service levels.
Preserving location privacy and keep the communication of UEs safe during
roaming is essential. Designing a secure and efficient authentication protocol
for roaming users in the mobile network is a challenging. In order to secure
communication over an insecure channel, authentication schemes have been
proposed. The main weakness of the existing authentication protocols is that
attackers have the ability to impersonate a legal user at any time. In
addition, the existing protocols are vulnerable to various kind of
cryptographic attacks such as insider attack, bit flipping attack, forgery
attacks, DoS attack, unfair key agreement and cannot provide user’s anonymity
(madhusudhan2020mobile, ; kang2021lightweight, ; mafakheri2021smart, ;
weedage2023resilience, ) and in case of a successful attack the attacker can
achieve the serial ID of user and track it even after the attack is over.

####  4.3.6. Cloud-based Services.

Many location-based services rely on cloud-based infrastructure and data
centers for storage, processing, and analysis of location data. While cloud
computing offers scalability and accessibility, it also raises concerns about
data sovereignty, data ownership, and third-party access to sensitive location
information. Securing location data in cloud environments requires encryption,
data anonymization, and strict access controls to prevent unauthorized access
or data breaches.

###  4.4. A Common Scenario: False Base Station Detection Techniques

The term ”false base stations” (FBS) refers to attacks where an imposter base
station pretends to be a legitimate one. This issue is currently being studied
by the SA3 working group, as documented in TR 33.809 [1]. In 5G, an FBS
typically acts as a ”man-in-the-middle” (MitM) or a very covert jammer. A
significant vulnerability exposed by FBSs is that the initial phases of
network entry, which occur before the implementation of 5G security protocols,
are especially susceptible to many of the attacks. For instance, attacks that
involve replaying altered versions of broadcast channels can severely impact
all the terminals in a cell, either disrupting their network connection or
forcing them to operate in a compromised state. Therefore, it is crucial to
develop methods that enable user equipment (UE) to verify the legitimacy of a
base station before exchanging unauthenticated messages by incorporating a
base station authentication factor. To achieve this, Physical Layer Security
(PLS) could be utilized for localization by a UE as a form of soft
authentication (chorti2022context, ; anant2023introduction, ;
kazmi2023security, ; irram2022physical, ).

##  5\. Privacy-Preserving Mechanisms

As mobile networks evolve into B5G/6G, the volume and precision of location
data being generated increases dramatically, making the protection of user
privacy more critical than ever. The advancement in technologies, such as
mmWave and MIMO in 5G, and the integration of AI and satellite communications
in 6G, have amplified the need for robust privacy-preserving mechanisms. This
section delves into the various strategies and technologies currently employed
to preserve location privacy, evaluates their effectiveness, and discusses
emerging solutions aimed at addressing the unique challenges posed by the
latest generation of mobile networks. Figure.6 illustrates the taxonomy of
various attacks targeting location privacy within B5G/6G networks. It
categorizes the different types of attacks that exploit vulnerabilities at
various layers of the network infrastructure

Figure 6. Attacks Toxonomy in B5G/6G

###  5.1. Current Advanced Privacy Techniques Solutions

A variety of technological, governmental, and legal approaches are now being
used to protect the integrity and confidentiality of location data in order to
offer location privacy.

####  5.1.1. Anonymization and Psuedonymization.

Homomorphic encryption, differential privacy, and pseudonymization are key
methods used to anonymize network data, including location data, during
transmission. Homomorphic encryption enables computations on encrypted data
without needing decryption, thereby maintaining data privacy even during
processing. Differential privacy adds random noise to data sets, ensuring that
individual data points cannot be re-identified while allowing for aggregate
data analysis. Pseudonymization replaces identifiable information with
pseudonyms, reducing the risk of re-identification and enhancing privacy.
These methods are often used in combination to provide robust protection
against privacy breaches. For instance, differential privacy can be applied to
pseudonymized data to further obscure individual identities, while homomorphic
encryption can secure data during both storage and computation phases.
Together, these techniques offer a layered approach to data protection,
protecting sensitive information from unauthorized access and ensuring
compliance with stringent data privacy regulations (anant2023introduction, ;
haque2021anonymous, ; sicari20205g, ; lin2024single, ). This combination of
advanced cryptographic and anonymization techniques is crucial for maintaining
the privacy and security of location data in modern communication networks.

####  5.1.2. Authentication.

Authentication is essential to a comprehensive strategy for protecting
location privacy. By verifying that only authorized users and devices have
access to location data, employing sophisticated authentication techniques,
and incorporating secure protocols, organizations can greatly reduce the risks
of unauthorized tracking and data breaches. Alongside other privacy-enhancing
technologies and regulatory measures, strong authentication systems are vital
for ensuring user privacy in current and future communication networks
(haque2021anonymous, ; sicari20205g, ; dawar2024enhancing, ; ma2023uav, ).

####  5.1.3. Location Masking.

Techniques for location masking typically involve obfuscating precise location
coordinates or substituting them with less accurate or more generic
identifiers to enhance user privacy. These strategies aim to balance the
utility of location data with robust privacy protections, ensuring individuals
cannot be easily tracked or profiled. One common method is differential
privacy, which adds random noise to location data, hiding true locations
within a range of possible values. Another approach is spatial cloaking, where
precise coordinates are replaced with broader areas like grid cells or
regions, maintaining data relevance for regional analyses while obscuring
individual details. Temporal cloaking reduces the frequency of location
updates, preventing detailed tracking of movement patterns. Advanced
cryptographic techniques, such as homomorphic encryption, allow for processing
location data while it remains encrypted, ensuring sensitive information is
not exposed. Dynamic location cloaking adjusts the level of obfuscation based
on context, offering higher protection for sensitive areas. These masking
strategies maintain the usefulness of location data for aggregate analysis and
operational purposes while significantly reducing the risk of unwanted
tracking and profiling, adhering to privacy regulations and user expectations
(sung2020zipphone, ).

####  5.1.4. Privacy-by-Design.

Privacy-by-design principles are promoted when location-based services are
designed and developed with privacy considerations integrated from the
beginning. This proactive approach ensures that privacy is not an afterthought
but a fundamental aspect of the development process. By incorporating privacy-
enhancing features such as data minimization, developers can limit the amount
of personal data collected and processed to only what is necessary for the
intended purpose, thereby reducing the potential for data breaches and misuse.
Purpose limitation ensures that data is used exclusively for the reasons
explicitly stated to users, preventing unauthorized secondary uses that could
compromise privacy. Additionally, user-centric controls empower individuals to
manage their privacy settings actively, giving them greater control over their
personal information. These controls can include options for users to opt-in
or opt-out of data collection, specify data retention periods, and control who
can access their data. By embedding these principles into the core
architecture of location-based services, developers can significantly mitigate
privacy risks, enhance user trust, and ensure compliance with stringent
regulatory frameworks such as GDPR and CCPA. This holistic approach not only
protects user privacy but also fosters a culture of transparency and
accountability in the development and deployment of location-based
technologies (masouros2023towards, ).

####  5.1.5. Improving Awareness.

People can make informed choices regarding their privacy preferences when they
are told about the dangers and consequences of location tracking and receive
guidance on privacy-enhancing techniques. User-friendly privacy controls,
privacy literacy programs, and awareness efforts increase public knowledge of
location privacy issues while promoting responsible data stewardship.

####  5.1.6. Regulation.

Regulation refers to a set of rules, policies, and laws established by
government authorities or regulatory bodies to govern and control various
aspects of 5G technology. These regulations are designed to ensure that the 5G
operates in a safe, fair, and orderly manner, with the goal of protecting the
interests of consumers, fostering competition, and addressing potential risks
or challenges (bauer2018roles, ; morgado2018survey, ). Regulations can cover a
wide range of areas, including: Spectrum allocation, network security,
privacy, competition, and environmental impact. Legal requirements for
protecting location data and preserving user privacy are established by
regulatory frameworks such as the Federal Communications Commission (FCC) and
California Consumer Privacy Act (CCPA) in the US and the General Data
Protection Regulation (GDPR) in Europe. To ensure compliance with these
requirements, privacy measures must be put in location, privacy impact
assessments must be carried out, and data processing procedures must be
transparent and accountable (anant2023introduction, ; bauer2022regulation, ;
morgado2018survey, ).

####  5.1.7. ML-based Solution.

ML algorithms can analyze vast amounts of data to identify patterns and
anomalies that may indicate unauthorized access or malicious activities. For
instance, ML can improve the effectiveness of anomaly detection systems by
continuously learning from new data and evolving threats, making it harder for
attackers to evade detection. Additionally, ML can be used in adaptive and
contextual authentication systems, where it can dynamically adjust security
measures based on the user’s behavior and context, significantly enhancing
security while maintaining user convenience (pandey2024privacy, ;
ferrag2018security, ; rafique2023securemed, ).

Figure 7. Location Privacy Mechanisms

###  5.2. Emerging Solutions for B5G/6G

Preserving privacy in B5G and 6G networks is crucial as these networks are
expected to handle an unprecedented amount of sensitive data from a vast array
of connected devices such as IoX and VANET networks. To preserve privacy to
high amount of detailed data, new solutions have been proposed by the
community. Some of these methods, such as homomotphic encryption are the same
as previous versions of network. Encryption methods are vital to provide data
security in hierarchy of network and have a long history. But new techniques
to cope with high bitrate and energy effeciency standards of 6G are necessary.
Figure. 7 illustrates a comparison between the effectiveness and complexity of
adoption of various privacy-enhancing solutions in B5G/6G networks. The figure
presents a visual assessment of different solutions like differential privacy,
blockchain, multi-party computation, and secure multi-access edge computing,
highlighting their relative strengths in ensuring user privacy versus the
complexity involved in implementing these technologies. The axes of the graph
show the balance between the solution’s effectiveness in protecting privacy
and the complexity or effort required to adopt and integrate these solutions
into existing and future network infrastructures.

####  5.2.1. Adaptive and Contextual Authentication.

Adaptive and contextual authentication systems enhance security by dynamically
adjusting authentication requirements based on various factors, such as user
behavior, device characteristics, and environmental context. These systems
continuously analyze real-time data to assess the risk level associated with
each authentication attempt. For instance, if a user attempts to log in from
an unusual location or at an atypical time, the system might prompt for
additional verification steps, such as multi-factor authentication, to ensure
the legitimacy of the access request. This approach balances security and
usability by tightening security controls in high-risk situations while
minimizing friction in low-risk scenarios. By leveraging machine learning and
advanced analytics, adaptive and contextual authentication provides a robust
defense against unauthorized access, making it particularly suitable for
applications requiring high security, such as financial services and sensitive
data management (bumiller2023understanding, ; zhao2021open, ; le2022novel, ).

####  5.2.2. Federated Learning.

FL is a decentralized machine learning approach that is particularly well-
suited for addressing the data privacy and computational challenges of 6G
networks. In the context of 6G, FL allows multiple devices, such as
smartphones and IoT devices, to collaboratively train a machine learning model
without sharing their raw data. Instead of sending raw data to a central
server, only the model updates are shared, significantly reducing the risk of
data breaches and preserving user privacy. FL is particularly beneficial for
applications like predictive maintenance in smart cities or personalized
healthcare, where sensitive data is involved. FL can also be integrated with
edge computing, enabling local data processing and reducing latency, which is
crucial for real-time applications like autonomous driving, augmented reality,
and smart healthcare. By optimizing network resources through the prediction
of traffic patterns and management of network load, FL can enhance the QoS and
reduce operational costs. Moreover, FL enables the development of personalized
services by training models on-device using user-specific data, which enhances
user experience in applications like personalized recommendations, health
monitoring, and smart home automation (yang2022federated, ; priya20246g, ).

FL also contributes to robust security mechanisms, such as anomaly detection
and fraud prevention, by collaboratively detecting potential security threats
across the network without compromising individual user data. Its application
in federated authentication enhances security protocols, making systems more
resilient to attacks. Additionally, FL improves the performance and
intelligence of IoT applications in smart cities, industrial automation, and
environmental monitoring. Despite these benefits, FL faces challenges such as
communication overhead, heterogeneous device capabilities, data and model
security, and ensuring model convergence across diverse datasets and devices.
Addressing these challenges is essential to fully realize the potential of FL
in 6G networks.

####  5.2.3. Homomorphic Encryption.

Homomorphic encryption approaches are useful in scenarios where data needs to
be processed by third-party services without compromising privacy, such as in
cloud computing and secure data sharing among organizations. But as Quantom
computing becomes reality of B5G/6G, quantum-resistant encryption methods are
being developed to protect data against quantum attacks. These encryption
methods are still in the research phase and may require significant
computational resources (narayanan2008robust, ).

####  5.2.4. Differential Privacy.

Differential privacy introduces noise into datasets to obscure individual
entries, making it difficult to identify specific individuals while still
allowing for meaningful aggregate data analysis. This technique is valuable
for data analytics and machine learning applications where it is crucial to
derive insights from data without exposing personal information. For instance,
differential privacy can be used in smart grids to analyze energy consumption
patterns without revealing individual household usage (wu2020location, ).

####  5.2.5. Blockchain and Distributed Ledger Technologies.

These technologies provide a decentralized and secure way to manage data
transactions and access. By using blockchain, data can be securely shared and
accessed in a transparent manner, with tamper-evident records ensuring data
integrity. This is particularly useful for applications like supply chain
management, where multiple parties need to access and verify transaction
records without compromising privacy (soni2023blockchain, ;
rafique2023securemed, ; priya20246g, ).

####  5.2.6. Multi-Party Computation (MPC).

MPC allows multiple parties to collaboratively compute a function over their
inputs while keeping those inputs private. This technique ensures that
sensitive data is not revealed to other parties during the computation
process. MPC can be applied in scenarios such as collaborative research, where
institutions need to share data insights without exposing proprietary
information (luz2024secure, ).

####  5.2.7. Secure Multi-Access Edge Computing (MEC).

MEC brings computation closer to the data source, reducing latency and
enhancing privacy by processing data locally on edge devices rather than
sending it to central servers. This approach minimizes the exposure of
sensitive data to potential breaches during transmission and central
processing. It is particularly beneficial for applications like autonomous
vehicles and industrial IoT, where real-time processing and privacy are
critical (liyanage2021driving, ).

These new solutions collectively enhance the ability of B5G and 6G networks to
protect user privacy while enabling advanced data processing and analytics,
ensuring that the benefits of these next-generation networks can be fully
realized without compromising security and trust. Figure 7 illustrating a
comparison between these solution’s effectiveness and complexity of adaption.

Anonymization and pseudonymization methods are generally effective at
preventing direct identification of individuals but can be vulnerable to re-
identification attacks when combined with auxiliary data, offering an
effectiveness rated at 70%. Location masking and blurring techniques reduce
the precision of location data to protect privacy but degrade the quality of
location-based services, with an effectiveness rated at 60%. Privacy-enhancing
technologies (PETs) are highly effective at protecting privacy by minimizing
data exposure, though they can be computationally intensive, achieving an
effectiveness rating of 90% (narayanan2008robust, ).

Geofencing and location-based access controls are effective at controlling
access based on location but rely on accurate location data and can be
spoofed, with an effectiveness rated at 80%. User consent and transparency
mechanisms ensure that users are aware of and consent to data usage, though
they can suffer from consent fatigue, also rated at 80% effectiveness.
Privacy-by-design principles are highly effective when implemented from the
ground up but require significant organizational change, rated at 90%.
Decentralized authentication systems improve security and privacy through
decentralization, though they can be complex and challenging to scale, rated
at 80% effectiveness. Adaptive and contextual authentication methods are very
effective at ensuring security based on context, though resource-intensive,
rated at 90%. Edge computing and fog networking enhance privacy by processing
data closer to the source but introduce new security challenges due to their
distributed nature, rated at 75%. Quantum-resistant encryption promises very
high security against future quantum attacks, though still largely
experimental, rated at 95% effectiveness (alshantti2024privacy, ;
bashir2024progress, ; narayanan2008robust, ).

##  6\. Discussion

###  6.1. Physical Layer Security for B5G/6G Privacy

Physical layer security is critical in preserving location privacy within
B5G/6G networks. The physical layer is where communication signals are
transmitted and received, making it a prime target for various attacks. Key
attacks that exploit vulnerabilities at this layer include:

Man-in-the-Middle (MitM) Attacks: In these attacks, an adversary intercepts
and potentially alters the communication between the user and the network
without either party’s knowledge. By doing so, attackers can gain access to
sensitive location data and other personal information. MitM attacks can be
particularly damaging in scenarios where location data is used for sensitive
applications such as financial transactions or health services.

Jamming: This involves deliberate interference with communication signals,
causing degradation in service quality. Attackers can use jamming to disrupt
legitimate connections, forcing devices to reconnect to malicious base
stations or fallback to less secure protocols. This can lead to the exposure
of location data and other personal information.

Fingerprinting: Attackers analyze the unique characteristics of signals
emitted by devices to identify and track them over time. By creating a profile
of a device’s signal patterns, attackers can track a user’s movements and
activities, compromising their privacy. For example, if an intruder
successfully fingerprints a 5G base station, they can gather a variety of
information that can be used for various purposes, some potentially malicious.
This information includes the Cell Identifier (Cell ID), Public Land Mobile
Network (PLMN) ID, Tracking Area Code (TAC), Global Cell ID (GCI), and
Physical Cell ID (PCI), which provide detailed insights into the network’s
layout and geographic scope. They can also collect data on the frequency bands
and specific channels used, supported technologies like 4G LTE and 5G NR, and
base station capabilities such as MIMO and carrier aggregation. Additionally,
they can map neighboring cells and assess signal strength and quality metrics.
The potential for misuse of this information includes detailed mapping of the
network for planning attacks, creating targeted jamming attacks, setting up
rogue base stations for MITM attacks, and exploiting weaknesses in handover
procedures. It can also facilitate location tracking of users and manipulation
of network behavior (nazerian2021passive, ; li2019passive, ).

Fake Base Stations (FBS): Also known as rogue base stations, these are
unauthorized devices that mimic legitimate base stations to deceive UE into
connecting to them. Once connected, these fake base stations can capture
location data and other sensitive information. FBS attacks highlight a
significant vulnerability, especially during the initial network entry phase
before the establishment of secure communication protocols. To mitigate these
threats, it is crucial to implement robust physical layer security measures.
Techniques such as advanced encryption, secure authentication, signal
obfuscation, and the use of spread spectrum technologies can enhance security
at the physical layer. Ensuring that devices and base stations authenticate
each other before exchanging sensitive information can also prevent
unauthorized access.

###  6.2. Decentralized Systems for Privacy Protection

Distributed systems and technologies like blockchain offer significant
potential for enhancing location privacy in B5G/6G networks. These
technologies help decentralize authentication processes and data storage,
reducing the risks associated with centralized systems. For example,
distributed authentication approach involves authenticating users and devices
across multiple, independent nodes rather than relying on a single centralized
authority. This reduces the risk of a single point of failure and makes it
more difficult for attackers to compromise the authentication process. By
using distributed authentication, the system can ensure that even if one node
is compromised, the overall integrity and security of the network remain
intact (soni2023blockchain, ; nyangaresi2023privacy, ).

Further, blockchain provides a decentralized and tamper-proof ledger for
recording authentication transactions. It ensures transparency and security by
recording each authentication attempt in an immutable ledger. This can help
verify user identities without revealing sensitive location data, thereby
enhancing privacy. Blockchain can also support smart contracts to enforce
privacy policies automatically and securely. Storing user identity separately
from location data means that even if an attacker gains unauthorized access to
one set of data, they cannot easily correlate it with other personal
information. This separation minimizes the potential damage from data breaches
and enhances overall privacy. For instance, identity verification can be
handled by one part of the network, while location data processing occurs
elsewhere, reducing the risk of comprehensive data leaks (priya20246g, ;
luz2024secure, ).

###  6.3. Robust Security with Satellite Communication

Satellite communication introduces a robust layer of security that can enhance
location privacy. Satellites are physically more difficult to access and
tamper with compared to terrestrial infrastructure. This inherent security
makes it significantly harder for attackers to conduct MitM attacks, deploy
fake base stations, or perform jamming. Satellite communications often employ
sophisticated and proprietary encryption methods that add a significant layer
of security. These encryption techniques make it exceedingly difficult for
attackers to intercept and decipher location data. Additionally, satellite
networks can dynamically change encryption keys and use advanced cryptographic
protocols to further enhance security. Satellites provide consistent and
secure communication across vast geographic areas, ensuring that users in
remote or underserved regions can benefit from enhanced privacy protections.
This global coverage also enables more reliable and resilient communication,
reducing the chances of location data being exposed through weak terrestrial
connections (beyaz2024satellite, ).

###  6.4. User-Centric Privacy Awareness & Regulatory Oversight

Improving user awareness and strengthening regulatory oversight are crucial
components in preserving location privacy. Users need to be informed about the
risks associated with location data and the measures they can take to protect
their privacy. Educating users on the importance of regularly reviewing and
updating their privacy settings on devices and applications. Providing clear
instructions and tools to manage these settings can empower users to take
control of their privacy. Offering guidelines on safe internet practices, such
as recognizing phishing attempts, avoiding suspicious networks, and using
encrypted connections. Promoting the use of privacy-enhancing tools, such as
VPNs and secure communication apps, can also help protect location data.

Regulatory entities must also recognize the advancements in cellular networks
as critical infrastructure and update privacy regulations accordingly.
Ensuring that privacy regulations keep pace with technological advancements in
B5G/6G networks. Implementing strict enforcement mechanisms to hold MNOs
accountable for breaches of privacy by conducting regular audits, imposing
penalties for non-compliance, and requiring transparency in data handling
practices. Also, encouraging the adoption of privacy-by-design principles in
the development and deployment of new technologies can be done by regulations.
This approach ensures that privacy considerations are integrated into the
design and operation of systems from the outset, rather than being added as an
afterthought. By combining technological, educational, and regulatory efforts,
we can create a comprehensive approach to preserving location privacy in the
evolving landscape of B5G/6G networks (benlloch2023distributed, ; nokia, ).

##  7\. Conclusion & Overlook

The evolution from 4G to B5G/6G networks marks a significant technological
leap, bringing about revolutionary changes in connectivity, speed, and
application potential. However, these advancements also introduce new
challenges, particularly regarding the privacy of location data. As these
networks provide higher precision in tracking and a vast increase in connected
devices, the potential for privacy breaches escalates. In this paper, we
explored the critical importance of location privacy in the context of B5G/6G
networks. We discussed the architectural advancements and their implications,
highlighting how new technologies such as NFV, SDN, MIMO, and others, while
enhancing network capabilities, also pose privacy risks. We identified and
evaluated various location privacy mechanisms, recognizing the limitations and
effectiveness of current solutions and provide about adapting complexity of
these methods. We shouldn’t overlook the crucial role of ML in both presenting
challenges and offering solutions within the context of B5G/6G location
privacy. ML algorithms can be exploited for advanced tracking and analysis,
potentially compromising user privacy by predicting and inferring location
patterns with high accuracy. However, the same technology can also be
harnessed to enhance privacy protections. ML can enable sophisticated anomaly
detection systems that identify and mitigate suspicious activities, implement
adaptive and contextual authentication mechanisms, and optimize data
anonymization techniques. Thus, while ML introduces new vulnerabilities, it
also offers powerful tools to fortify privacy measures, necessitating a
balanced and strategic approach in its deployment within next-generation
networks.

We also emphasized the need for robust physical layer security and advanced
tracking methods, noting the potential threats from attacks like
fingerprinting, man-in-the-middle, jamming, and fake base stations. The
discussion extended to the benefits of distributed authentication systems and
the potential role of satellite communication in improving location privacy.
Finally, we underscored the importance of regulatory measures and increasing
user awareness to mitigate privacy risks. As we move towards a highly
connected and automated world, preserving location privacy remains an open and
pressing challenge. Continued research and innovative solutions are imperative
to ensure that the benefits of B5G/6G networks are realized without
compromising user privacy.

## Acknowledgments

The authors would like to acknowledge the use of AI-based tools, specifically
ChatGPT (chatgpt, ) developed by OpenAI, for assistance in correcting
grammatical errors and improving the writing of this paper. The AI tool was
utilized to revise the text throughout the paper, particularly focusing on
correcting any typos, grammatical errors, and awkward phrasing.

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