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Shaham, S., Ding, M., Liu, B., Dang, S., Lin, Z. and Li, J. (2019) Privacy Preserving Location Data Publishing: A Machine Learning Approach. IEEE Transactions on Knowledge and Data Engineering, 1. arXiv:1902.08934
http://arxiv.org/abs/1902.08934
https://doi.org/10.1109/TKDE.2020.2964658

has been cited by the following article:

• TITLE: A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

  AUTHORS: Zhijian Shao, Bingwen Feng, Xingzheng Li

  KEYWORDS: Internet of Vehicles, Privacy Protection, Trajectory Data, Markvo Matrix

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.4, April 16, 2021

  ABSTRACT: Trajectory data set is the indispensable foundation for constructing reliable Internet of Vehicles (IoV) service and location-based service (LBS), while it is likely to be abused by malicious attackers to infer user’s privacy. In this paper, we propose a trajectory protection method based on stop points obfuscation, which can confront various privacy attacks and preserve the semantic information to achieve adequate utility. Two strategies for stop point selection are designed, including category-distance priority method and Markov matrix method. Our new method was analyzed and evaluated on a real-world trajectory data set. The experiment result shows that our method can improve the utility of the data set and provide multi-level privacy protection.