Open Access
ARTICLE
A Differential Privacy Based (k-Ψ)-Anonymity Method for Trajectory Data Publishing
Hongyu Chen1, Shuyu Li1, *, 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.
Keywords
Cite This Article
H. Chen, S. Li and Z. Zhang, "A differential privacy based (
k-Ψ)-anonymity method for trajectory data publishing,"
Computers, Materials & Continua, vol. 65, no.3, pp. 2665–2685, 2020.