TCCM: Trajectory Converged Chaff-Based Mix-Zone Strategy for Enhancing Location Privacy in VANET

Abstract

Vehicles in vehicular ad-hoc networks (VANETs) are required to continuously broadcast sensitive data including coordinates and speeds. Such practices make sensitive data susceptible to eavesdroppers, undermining location privacy. Conventional strategies attempt to preserve location privacy through different privacy enhancing techniques such as encryption, silent periods, and chaff messages. However, existing approaches fail to simultaneously ensure driving safety and location privacy, particularly against semantic linking attacks by machine-learning-enabled adversaries. To this end, this paper proposes the trajectory converged chaff-based mix zone (TCCM) strategy. It generates chaff messages that imitate real vehicle trajectories to confuse eavesdroppers while maintaining low communication overhead, thereby enhancing location privacy without compromising vehicle safety. Additionally, the TCCM strategy incorporates a genetic algorithm to optimize mix zone placement and ensure a balance between privacy protection and resource efficiency. We implemented a VANET simulation and two adversary attack algorithms to evaluate the performance of our scheme. Reportedly, the TCCM strategy reduces trajectory traceability by at least 24.6% compared with conventional mix zone strategies while maintaining vehicle safety. Additionally, the chaff messages of the TCCM strategy incur 54% less communication overhead than existing chaff-based schemes.