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Intelligent Transportation Systems (ITS) optimize road network capacity, monitor traffic flow, and enhance overall road safety by analyzing real-time trajectory data. However, the utilization of such data raises privacy concerns, enabling potential attackers to gain insights into users' real-time activities and personal information. Furthermore, existing privacy preservation methods have multiple limitations, particularly in low-traffic density environments. To address these issues, this paper presents a novel approach for generating realistic trajectories that evade tracking. Existing trajectory generation mechanisms are coarse-grained and cannot adequately preserve the quality of location-based services while safeguarding individual privacy. To overcome this limitation, we first use differential privacy to determine a location near the actual destination and employ a path search algorithm to extract relevant road information. Subsequently, by leveraging our hybrid reinforcement learning model, we generate trajectories leading to this fictitious point. The comparison conducted on real-world maps with other trajectory generation methods reveals its superior ability to preserve spatio-temporal features. Finally, we propose two approaches that use the generated trajectories to protect privacy, ensuring both individual privacy protection and the utility of data.
Zhang et al. (Thu,) studied this question.