@Article{cmc.2020.010965,
AUTHOR = {Hongyu Chen, Shuyu Li, Zhaosheng Zhang},
TITLE = {A Differential Privacy Based (<i>k</i>-Ψ)-Anonymity Method for  Trajectory Data Publishing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2665--2685},
URL = {http://www.techscience.com/cmc/v65n3/40197},
ISSN = {1546-2226},
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 <i>k</i>-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 (<i>k</i>-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 (<i>k</i>-Ψ)-anonymous trajectory 
data publishing algorithm is given, which improves (<i>k</i>-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.},
DOI = {10.32604/cmc.2020.010965}
}