Article citationsMore>>

Zhang, M., Li, X., Miao, Y., Luo, B., Ren, Y. and Ma, S. (2024) PEAK: Privacy-Enhanced Incentive Mechanism for Distributed—Anonymity in LBS. IEEE Transactions on Knowledge and Data Engineering, 36, 781-794.
https://doi.org/10.1109/TKDE.2023.3295451

has been cited by the following article:

• TITLE: A Trajectory Privacy Protection Method to Resist Long-Term Observation Attacks

  AUTHORS: Qixin Zhan

  KEYWORDS: Location Privacy, Long-Term Observation Attacks, K-Anonymity, Location Caching

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.5, May 22, 2024

  ABSTRACT: Users face the threat of trajectory privacy leakage when using location-based service applications, especially when their behavior is collected and stored for a long period of time. This accumulated information is exploited by opponents, greatly increasing the risk of trajectory privacy leakage. This attack method is called a long-term observation attack. On the premise of ensuring lower time overhead and higher cache contribution rate, the existing methods cannot utilize cache to answer subsequent queries while also resisting long-term observation attacks. So this article proposes a trajectory privacy protection method to resist long-term observation attacks. This method combines caching technology and improves the existing differential privacy mechanism, while incorporating randomization factors that are difficult for attackers to recognize after long-term observation to enhance privacy. Search for locations in the cache of both the mobile client and edge server that can replace the user’s actual location. If there are replacement users in the cache, the query results can be obtained more quickly. Simultaneously obfuscating the spatiotemporal correlation of actual trajectories by generating confusion regions. If it does not exist, the obfuscated location generation method that resists long-term observation attacks is executed to generate the real anonymous area and send it to the service provider. The above steps can comprehensively protect the user’s trajectory privacy. The experimental results show that this method can protect user trajectories from long-term observation attacks while ensuring low time overhead and a high cache contribution rate.