Huaxiong Liao1,2, Xiangxuan Zhong2, Xueqi Chen2, Yirui Huang3, Yuwei Lin2, Jing Zhang2,*, Bruce Gu4
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069208
- 10 November 2025
Abstract The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns. However, the use of real-world trajectory data poses significant privacy risks, such as location re-identification and correlation attacks. To address these challenges, privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data. This paper introduces DPIL-Traj, an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation. Firstly, the framework incorporates Differential Privacy Clustering, which anonymizes trajectory data by applying differential privacy techniques that add noise, ensuring the… More >
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