An optimized differential privacy scheme with reinforcement learning in VANET

Abstract

The protection of vehicle trajectory in Vehicular ad hoc network is facing many challenges. Among these challenges, one of the most critical issues is to keep the balance between geographical location protection and semantic location protection. Traditional trajectory protection schemes either only focus on geographical location protection or only semantic location protection. Moreover, when trajectory privacy protection is carried out, each location is often given the same protection. This may lead to sensitive locations under insufficient protection and unimportant locations under overprotection. In this paper, based on differential privacy, we propose an optimized privacy differential privacy scheme with reinforcement learning in vehicular ad hoc network. The proposed scheme can dynamically optimize the privacy budget allocation for each location on the vehicle trajectory to reach a better balance between geolocation obfuscation and semantic security. Experiments results demonstrate that the proposed scheme can reduce the risk of geographical and semantic location leakage, and therefore ensure the balance between the utility and privacy.