Multi-Level Location Privacy Protection Based on Differential Privacy Strategy in VANETs

Location-based service (LBS) has been widely used and brings convenience to people's lives. In order to obtain the desired services, a user must report its current location information to the LBS provider. If this information falls into the hands of malicious adversaries, users may even face serious threats. Researchers have proposed some LBS-based vehicle location privacy protection methods. However, these methods are vulnerable to attackers with background knowledge. In this paper, we propose a privacy protection method based on the background knowledge of reliable servers, and use correlation probabilities and correlation transition probabilities to achieve ε-differential privacy geography indistinguishability. The Laplace scheme is used to add noises to the query results. At the same time, this method provides different levels of privacy protection. The simulation results compare and explain the incompleteness of the consideration of this algorithm.