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Dummy trajectory generation scheme based on generative adversarial networks

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Abstract

Dummy trajectory is widely used to protect the privacy of mobile users’ locations. However, two main challenges remain: (1) Map background information has not been modeled by machine learning methods in existing schemes, and (2) it is difficult to generate a good quality dummy trajectory that is similar to the real one. Focused on these two challenges, in this paper, we propose a dummy trajectory generation scheme with conditional generative adversary network (GAN), where the map features are extracted using convolutional neural network, which is regarded as a prior restriction of conditional GAN. Then, the movement pattern of the real trajectory is deduced by an auto-encoder and is involved in the dummy trajectory generation. Our model is trained and evaluated with two real-world datasets. Experimental results demonstrate that our scheme addresses these challenges well and defends against various attacks effectively.

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Data availability

The datasets analyzed during the current study are available in Geolife project (https://www.microsoft.com/en-us/research/project/geolife-building-social-networks-using-human-location-history/) and T-Drive research (https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/).

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Funding

This work is supported in part by the National Natural Science Foundation of China (62072133) and in part by the Key Projects of Guangxi Natural Science Foundation (2018GXNSFDA281040).

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Authors and Affiliations

  1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Jinji Road, Guilin, 541004, Guangxi, China

    Jingkang Yang, Xiaobo Yu & Yining Liu

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Anker Engelunds Vej 101 2800, 10587, Kongens Lyngby, Denmark

    Weizhi Meng

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Correspondence to Yining Liu.

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Yang, J., Yu, X., Meng, W. et al. Dummy trajectory generation scheme based on generative adversarial networks. Neural Comput & Applic 35, 8453–8469 (2023). https://doi.org/10.1007/s00521-022-08121-4

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