ST-TrajGAN: A synthetic trajectory generation algorithm for privacy preservation

• Published in: Future generation computer systems Vol. 161; pp. 226 - 238
• Main Authors: Ma, Xuebin, Ding, Zinan, Zhang, Xiaoyan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: Generative adversarial networks; Location-based services; Trajectory privacy; Trajectory privacy; Location-based services; Generative adversarial networks
• ISSN: 0167-739X
• DOI: 10.1016/j.future.2024.07.011

·Create synthetic trajectory data with enhanced privacy protection.·Introducing a semantic trajectory encoding model for preprocessing trajectory points.·The transformer decoder performs well in modeling long-term dependencies.·Quantify the decrease in trajectory similarity using TrajLoss.·The ST-TrajGAN achieves an balance between protecting trajectory privacy and utility. The rapid growth of large-scale trajectory data poses privacy risks for location-based services (LBS), primarily through centralized storage and processing of data, as well as insecure data transmission channels (such as the Internet and wireless networks), which can lead to unauthorized access or manipulation of users' location information by attackers. To enhance trajectory privacy protection while improving the trajectory utility, this paper proposes an efficient and secure deep learning model Semantic and Transformer-based Trajectory Generative Adversarial Networks (ST-TrajGAN) for trajectory data generation and publication. First, this article introduces a semantic trajectory encoding model for preprocessing trajectory points. Through this model, trajectory points can be transformed into vector representations with semantic information. Next, by learning the spatio-temporal and semantic features of real trajectory data, a deep learning model is used to generate synthetic trajectories with more uncertainty and practicality. Furthermore, a novel TrajLoss loss metric function was crafted to gauge the trajectory similarity loss within the trained deep learning model. Ultimately, the efficacy of the generated synthetic trajectories and the model's utility are assessed through Trajectory-User Linking (TUL) and Trajectory Sharing Percentage (TSP) values on three authentic Location-Based Services (LBS) datasets. Numerous experiments have shown that our method outperforms other methods in terms of privacy protection effectiveness and utility.