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A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing.pdf (1.48 MB)

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

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posted on 31.01.2021, 14:52 by Fengmei Jin, Wen Hua, Matteo Francia, Pingfu Chao, Maria Orlowska, Xiaofang Zhou
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 systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset 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.

Funding

Making Spatiotemporal Data More Useful: An Entity Linking Approach

Australian Research Council

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Real-time Analytics on Urban Trajectory Data for Road Traffic Management

Australian Research Council

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History

Email Address of Submitting Author

fengmei.jin@uq.edu.au

ORCID of Submitting Author

0000-0003-3937-0511

Submitting Author's Institution

The University of Queensland

Submitting Author's Country

China

Licence

Exports