DRL-UPPS: User Trajectory Privacy Protection Strategy Based on Deep Reinforcement Learning in Mobile Crowdsensing

User trajectories are denser and highly dynamic in mobile crowdsensing (MCS) system, rendering traditional privacy budget allocation schemes insufficient. Additionally, the protection of semantic location privacy is often neglected in these schemes, making them vulnerable to inference attacks. To address these deficiencies, a user trajectory privacy protection strategy based on deep reinforcement learning is proposed in this article. First, a differential privacy-based user trajectory privacy protection algorithm (DP-upps) is designed to protect the privacy by perturbing the extracted trajectory feature points. Then, a deep reinforcement learning-based privacy budget allocation algorithm (DRL-pbas) is introduced. The privacy budget is dynamically adjusted by deep reinforcement learning option to continuously adapt to environmental changes and maximize benefits. After that, a DRL-pbas based user privacy protection strategy (DRL-UPPS) is proposed, integrating semantic location privacy protection. This approach combines the previous two algorithms, allowing the privacy budget to be allocated in a way that effectively balances the protection of physical and semantic location privacy and data quality. Ultimately, a large number of simulation experiments are conducted based on real datasets. The experiments demonstrate that DRL-UPPS can effectively balance privacy protection and data quality, resisting the privacy attacks. Compared with other strategies, DRL-UPPS improves comprehensive privacy protection capability by approximately 10% and data utility by approximately 8%.