Open Access

ARTICLE

A Differential Privacy Based (k-Ψ)-Anonymity Method for Trajectory Data Publishing

Hongyu Chen¹, Shuyu Li^{1, *}, Zhaosheng Zhang¹

1 School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China.

* Corresponding Author: Shuyu Li. Email: .

Computers, Materials & Continua 2020, 65(3), 2665-2685. https://doi.org/10.32604/cmc.2020.010965

Received 10 April 2020; Accepted 14 May 2020; Issue published 16 September 2020

Abstract

In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-Ψ)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided into two phases: in the first phase, a dummy-based (k-Ψ)-anonymous trajectory data publishing algorithm is given, which improves (k-δ)-anonymity by considering changes of threshold δ on different road segments and constructing an adaptive threshold set Ψ that takes into account road network information. In the second phase, Laplace noise regarding distance of anonymous locations under differential privacy is used for trajectory perturbation of the anonymous trajectory dataset outputted by the first phase. Experiments on real road network dataset are performed and the results show that the proposed method improves the trajectory indistinguishability and achieves good data utility in condition of preserving user privacy.

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Keywords

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