Open Access

ARTICLE

DPIL-Traj: Differential Privacy Trajectory Generation Framework with Imitation Learning

Huaxiong Liao^1,2, Xiangxuan Zhong², Xueqi Chen², Yirui Huang³, Yuwei Lin², Jing Zhang^2,*, Bruce Gu⁴

1 Faculty of Data Science, City University of Macau, Macau, 999078, China
2 School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, FujianUniversity of Technology, Fuzhou, 350118, China
3 Fujian Province Key Laboratory of Information Security and Network Systems, College of Computer Science and Big Data, Fuzhou University, Fuzhou, 350108, China
4 Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

* Corresponding Author: Jing Zhang. Email:

Computers, Materials & Continua 2026, 86(1), 1-21. https://doi.org/10.32604/cmc.2025.069208

Received 17 June 2025; Accepted 01 September 2025; Issue published 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 protection of sensitive user information. Secondly, Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories. By learning from expert trajectories, this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories. Finally, Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns. Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%, and in terms of privacy performance by an average of 12.51%, compared to state-of-the-art approaches. Ablation studies further reveal that DP clustering effectively safeguards privacy, imitation learning enhances utility under noise, and the Markov module strengthens temporal coherence.

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Keywords

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