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Resisting TUL attack: balancing data privacy and utility on trajectory via collaborative adversarial learning

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Abstract

Nowadays, large-scale individual trajectories can be collected by various location-based social network services, which enables us to better understand human mobility patterns. However, the trajectory data usually contain sensitive information of users, raising considerable concerns about the privacy issue. Existing methods for protecting user trajectory data face two major challenges. First, existing methods generally emphasize on data privacy but largely ignore the data utility. Second, most existing work focus on protecting the privacy of users’ check-in locations, which is not sufficient to protect against the trajectory-user linking (TUL) attack that infers a user’s identity based on her/his trajectories. In this paper, we for the first time propose a collaborative adversarial learning model named BPUCAL to effectively resist the TUL attack and preserve the data utility simultaneously. The general idea is to fool the TUL model by adding a small perturbation on the original trajectory data to balance the data utility and privacy. BPUCAL perturbs a few numbers of carefully identified check-ins of a trajectory which are pivotal for a TUL model to infer the identity of a user. Specifically, BPUCAL contains three parts: a perturbation generator, a discriminator, and a TUL model. The generator aims to produce learnable noise and adds it to the original trajectories for obtaining perturbed trajectories. The perturbed trajectories with a minimal changes compared to the original trajectories can deceive both the discriminator and the TUL model. Extensive experiments are conducted over two real-world datasets. The results show the superior performance of our proposal in balancing data privacy and utility on trajectory data by comparison with baselines.

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Availability of data and materials

The datasets can be find in http://sites.google.com/site/yangdingqi/home/foursquare-dataset and http://www.yongliu.org/datasets.html

Notes

  1. http://sites.google.com/site/yangdingqi/home/foursquare-dataset

  2. http://www.yongliu.org/datasets.html

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Funding

This work is supported by National Science Foundation of China (NO. 62172443 and 62372460), Hunan Provincial Natural Science Foundation of China (NO. 2022JJ30053), the financial support of Lingnan University (LU, NO. DB23A4), Lam Woo Research Fund at LU (NO. 871236), and the High Performance Computing Center of Central South University.

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Authors and Affiliations

  1. Central South University, Changsha, China

    Yandi Lun, Renzhi Wang & Senzhang Wang

  2. Aalborg University, Aalborg, Denmark

    Hao Miao

  3. Lingnan University, Hong Kong, China

    Jiaxing Shen

  4. National University of Defense Technology, Changsha, China

    Xiang Wang

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  1. Yandi Lun
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  2. Hao Miao
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  3. Jiaxing Shen
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  4. Renzhi Wang
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  5. Xiang Wang
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  6. Senzhang Wang
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Contributions

Yandi Lun wrote the main manuscript text and implemented the code. Senzhang Wang wrote the main manuscript text. Hao Miao participated in writing the introduction and conducted the comparison experiment. Renzhi Wang prepared Fig. 2 and conducted the Parameter Study. Jiaxing Shen discussed the idea of the work and wrote the related work. Xiang Wang conducted the ablation experiment. All authors reviewed the manuscript.

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Correspondence to Senzhang Wang.

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Lun, Y., Miao, H., Shen, J. et al. Resisting TUL attack: balancing data privacy and utility on trajectory via collaborative adversarial learning. Geoinformatica (2023). https://doi.org/10.1007/s10707-023-00507-3

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