Personalized Semantic Trajectory Privacy Protection in Location-Based Services: A TD3-Based Approach

The swift advancement of Location-Based Services (LBSs) raises the danger of trajectory privacy being breached, since the location semantic tags can easily disclose users' sensitive information. Additionally, attackers can exploit temporal correlations to infer sensitive personal information. This paper formulates a personalized semantic trajectory privacy protection framework designed to protect locations with varying sensitivities on the trajectory from the attacker with temporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the Reinforcement Learning (RL) technique to dynamically adjust the privacy parameters. Specifically, we leverage the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Simulation results indicate that the TD3-based personalized semantic trajectory privacy protection mechanism effectively balances the Quality of Service and semantic trajectory privacy while realizing personalized trajectory privacy protection.