Trajectory Obfuscation and Detection in Internet-of-vehicles

In Internet-of-vehicles, vehicles cooperate with each other by transmitting Internet-of-vehicles and location-based service (LBS) providers optimize services by analyzing trajectory data collected from drivers. Nevertheless, illegal trajectories generated by attackers or malicious drivers can obfuscate the process of analysis and breach the quality of service. Some mechanisms protect drivers’ location privacy by using obfuscation-based schemes. Obfuscation-based mechanisms report LBS with obfuscated trajectories data rather than actual trajectories, which increases difficulties to detect illegal trajectories accurately. This paper focuses on detecting illegal trajectories when all drivers employ obfuscation-based mechanisms to protect location privacy. In this paper, we propose a dynamic obfuscation mechanism in road networks based on Geo-indistinguishability to dynamically protect drivers’ location privacy. Considering personalization in road networks, we also propose a classification mechanism to detect illegal trajectories in road networks. Illegal trajectories are generated based on real trajectories to simulate actions of malicious drivers and attackers. Experiment results in real road networks show that the classifier can detect illegal obfuscated trajectories with at least 94% Area Under the Curve (AUC) score, which outperforms than existing works in road networks.