What is it about?
Protecting location privacy in vehicular networks has been a critical research focus for almost two decades, driven by the need to safeguard road users from serious threats such as cyber-stalking, blackmailing, targeted advertising, and even physical harm. The ability to track a vehicle’s movements poses significant risks, making privacy preservation essential for the safe deployment of intelligent transportation systems. Among the various solutions explored, pseudonym change strategies have emerged as one of the most effective approaches, striking a balance between privacy protection, ease of implementation, and network functionality. These strategies periodically update vehicle identifiers to prevent continuous tracking while ensuring seamless communication in vehicular networks. In this paper, we evaluate, classify, and analyze pseudonym change strategies, examining their strengths, weaknesses, and practical applicability. We also review the simulation tools used to assess their effectiveness in real-world scenarios. By consolidating nearly two decades of research, this study provides a comprehensive reference to guide future advancements in robust, efficient, and scalable location privacy protection solutions for vehicular networks.
Featured Image
Photo by Ravi Palwe on Unsplash
Why is it important?
As vehicular networks become increasingly integrated into modern transportation systems, ensuring location privacy is crucial for protecting road users from tracking, cyber-stalking, and other privacy violations that could lead to serious consequences. Without effective privacy-preserving mechanisms, attackers could exploit location data to compromise user security, enabling targeted advertising, blackmailing, or even physical harm. This paper provides a comprehensive analysis of pseudonym change strategies, which are among the most widely studied solutions for protecting location privacy while maintaining the correct functionality of vehicular networks. Specifically, we: - Define privacy requirements in vehicular networks and review security and privacy standards that guide their protection. - Examine adversary models and the different types of attacks that threaten location privacy. - Review and classify 52 pseudonym change strategies proposed in the literature from 2007 to 2024, highlighting their design, effectiveness, and limitations. - Summarize key considerations for researchers developing new pseudonym change schemes, including network type, application context, attacker models, evaluation metrics, and simulation tools. - Analyze the real-world impact of pseudonym change strategies on both user safety and network performance. - Identify open challenges and propose directions for future research to improve privacy-preserving mechanisms in vehicular networks. By synthesizing nearly two decades of research, this study provides valuable insights for researchers, engineers, and policymakers seeking to develop more effective, scalable, and practical location privacy protection solutions for the next generation of intelligent transportation systems.
Perspectives
Despite significant advancements in pseudonym change strategies for location privacy in vehicular networks, real-world implementation remains a challenge. Many proposed solutions have been evaluated theoretically and through simulations, yet their practical feasibility and impact on network functionality require further study. Researchers have largely focused on designing robust mechanisms to prevent tracking by powerful adversaries but often overlook the operational constraints that real vehicular networks impose. Moving forward, several key challenges must be addressed to bridge the gap between theoretical research and deployment. First, privacy-preserving solutions must ensure that safety applications remain uncompromised—interruption of beacon transmissions, misleading kinetic data, or excessive pseudonym consumption could introduce new risks on the road. Second, privacy schemes must be designed with cost efficiency in mind, as pseudonym management often comes with financial implications that could impact user adoption. Third, routing protocols and service stability must be carefully considered, as pseudonym changes can interfere with communication protocols, cloud services, and internet connectivity. Furthermore, the standardization and regulatory framework for privacy in vehicular networks remains an open issue. While privacy protection is essential, it must be conditional and accountable—a system that allows criminals to exploit anonymity for unlawful activities would be counterproductive. Therefore, legislation should ensure a balance between privacy rights and the ability to trace misbehavior when necessary. Emerging technologies such as federated learning and blockchain also introduce new privacy challenges. While they promise security benefits, their integration with pseudonym change mechanisms requires careful assessment to avoid inadvertently linking identifiers and compromising privacy. Additionally, cross-layer tracking—linking network-layer identifiers such as MAC and IP addresses—remains an overlooked threat that must be accounted for in future designs. As vehicular networks continue to evolve, privacy solutions must be evaluated beyond simulation environments and tested in real-world scenarios to assess their scalability, robustness, and practicality. A deeper understanding of how much privacy is sufficient and the trade-offs between privacy, security, and functionality will be crucial in shaping next-generation privacy-preserving solutions for connected vehicles. Finally, standardized evaluation frameworks and clear documentation will facilitate more effective comparisons and implementation of pseudonym change strategies, ensuring their viability in intelligent transportation systems.
Leila BENAROUS
University of Laghouat
Read the Original
This page is a summary of: A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular Networks, ACM Computing Surveys, March 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3718736.
You can read the full text:
Contributors
The following have contributed to this page







