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User re-identification via human mobility trajectories with siamese transformer networks

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Abstract

People are keen to share their geospatial locations to access social activities or services via mobile internet, which provides a new perspective for us to understand human mobility. However, the collection of individual mobility data arouses great concern about privacy among the public. Specifically, anonymized mobility records can be used to imply the routine behavior of individuals such that people can be re-identified even if they share footprints with different accounts or platforms. In this context, this study explored the probability of re-identifying anonymized users with advanced deep learning techniques, only leveraging their trajectories collected during a long time period. Such a user re-identification task realizes the identification of anonymous users by mining the characteristics of anonymous trajectories. Prevailing methods adopt deep sequential learning models, such as recurrent neural networks (RNNs), to capture the inherent similarity between any two trajectories, replacing classic statistical models. Despite that, RNN-like models usually fail in learning effective knowledge from longer sequences, such as one’s visited locations in one week. To this end, we propose a novel model based on the Siamese Transformer network. The entire model comprises a discriminant module and retrieval module. The discriminant module uses the Transformer model to detect the characteristics of the trajectory and employs an improved attention mechanism to achieve similarity measurement between trajectories. The retrieval module helps the model deal with the matching between the anonymous trajectory and user by constructing a mapping relationship between users and locations. Extensive experiments on four real-world location-based social network datasets demonstrated that our method outperforms existing methods.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was jointly supported by the National Science Foundation of China (62102258), the Shanghai Pujiang Program (21PJ1407300), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), and the National Social Science Foundation Major Project of China (21 &ZD200). Thanks Yan Zhang for his contributions to the work.

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Authors and Affiliations

  1. College of Information, Mechanical, and Electrical Engineering, Shanghai Normal University, Shanghai, 201400, China

    Bin Wang, Mingming Zhang & Peng Ding

  2. Shanghai Urban and Rural Construction and Traffic Development Research Institute, Shanghai, 200240, China

    Tao Yang

  3. MoE Key Laboratory of Artificial Intelligence, AI Institute, and China Institute for Smart Court, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yaohui Jin & Yanyan Xu

Authors
  1. Bin Wang
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  2. Mingming Zhang
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  4. Tao Yang
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Contributions

Bin Wang: Methodology, Writing Original Draft; Mingming Zhang: Data Curation, Software, Methodology, Visualization; Peng Ding: Conceptualization, Formal Analysis; Tao Yang: Project Administration; Yaohui Jin: Supervision, Investigation; Yanyan Xu: Methodology, Supervision, Visualization.

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Correspondence to Yanyan Xu.

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We used publicly available datasets and there are no ethical concerns.

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Wang, B., Zhang, M., Ding, P. et al. User re-identification via human mobility trajectories with siamese transformer networks. Appl Intell 54, 815–834 (2024). https://doi.org/10.1007/s10489-023-05234-8

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