Trajectory Data Publication Based on Differential Privacy

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Author(s): Zhen Gu (Harbin Engineering University, China)and Guoyin Zhang (Harbin Engineering University, China)
Copyright: 2023
Volume: 17
Issue: 1
Pages: 15
Source title: International Journal of Information Security and Privacy (IJISP)
Editor(s)-in-Chief: Yassine Maleh (Sultan Moulay Slimane University, Morocco)and Ahmed A. Abd El-Latif (Menoufia University, Egypt)
DOI: 10.4018/IJISP.315593

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

Analyzing trajectory data can provide people with a higher quality of life. However, publishing trajectory data directly will leak privacy. The authors propose a trajectory data publication method based on differential privacy (TDDP). TDDP method consists of two stages. In the location generalization stage, firstly, the locations at each timestamp are clustered into classes by k-means++ algorithm, and then the representative location of each class is selected by using the exponential mechanism. In the generalized trajectory data publication stage, the authors design a sampling mechanism to form the generalized trajectories. The locations are sampled from the representative locations under different timestamps to form the generalized trajectories. The TDDP method can avoid the generation of non-semantic representative locations and ensure that the generalized trajectories can resist filtering attacks. The experimental results show that the trajectory data released by TDDP method can achieve a good balance between privacy protection and data availability.

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Trajectory Data Publication Based on Differential Privacy

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