A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, so...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2023-06, Vol.35 (6), p.1-1
Main Authors: Jin, Fengmei, Hua, Wen, Francia, Matteo, Chao, Pingfu, Orlowska, Maria, Zhou, Xiaofang
Format: Article
Language:English
Subjects:
attack models
Couplings
Data analysis
Data models
Data privacy
Location based services
Privacy
privacy metrics
privacy protection models
Publishing
Spatiotemporal phenomena
Trajectory
Trajectory analysis
Trajectory data publishing
utility metrics
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Summary:Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and a systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on two real-life public trajectory datasets to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3174204