Ensuring Traffic Trajectory Data Privacy Based on Opacity Enforcement by Reinforcement Learning

Abstract

The proliferation of mobile devices has made location-based services an integral part of daily life, yet continuous sharing of precise locations poses significant privacy risks, such as exposure to unwarranted surveillance. To address this challenge, this paper proposes a novel reinforcement learningbased method to generate an optimal insertion function under a private insertion mechanism, ensuring current-state opacity without requiring a formal model of user mobility. Using the publicly available Geolife GPS trajectory dataset, the densitybased spatial clustering of applications with noise algorithm, parameterized via a K-distance graph, is applied to extract meaningful location states from raw trajectories. Experimental results show that as training proceeds, the cumulative reward converges and the probability of secret exposure approaches zero. Furthermore, the resulting insertion automaton is isomorphic to that obtained by the baseline model-based method, demonstrating the effectiveness of our data-driven approach.