Protecting Semantic Trajectory Privacy for VANET with Reinforcement Learning

Location-based services in vehicular ad hoc networks (VANETs) have to protect user privacy and address the challenge due to the disclosure of the vehicle movement trajectory. In this paper, we propose an reinforcement learning (RL) based differential privacy mechanism that randomizes the released vehicle locations to protect the semantic trajectory of the vehicle and uses RL to select the obfuscation policy. Based on the semantic location of the vehicle and the attack history, this scheme enables a vehicle to optimize the obfuscation policy in terms of the privacy gain and the quality of service loss without being aware of the current attack model in a dynamic privacy protection game. Simulation results show that this scheme can increase the privacy gain, decrease the quality of service loss, and thus improve the utility of the vehicle in comparison with a benchmark scheme.