Open AccessPersonalized Privacy‐Preserving Trajectory Data Publishing
Author(s) -
Lu Qiwei,
Wang Caimei,
Xiong Yan,
Xia Huihua,
Huang Wenchao,
Gong Xudong
Publication year - 2017
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.01.024
Subject(s) - computer science , trajectory , data publishing , anonymity , popularity , the internet , privacy software , mobile device , information privacy , internet privacy , cluster analysis , scheme (mathematics) , k anonymity , adversarial system , world wide web , computer security , data mining , publishing , artificial intelligence , psychology , social psychology , physics , astronomy , political science , law , mathematical analysis , mathematics
Due to the popularity of mobile internet and location‐aware devices, there is an explosion of location and trajectory data of moving objects. A few proposals have been proposed for privacy preserving trajectory data publishing, and most of them assume the attacks with the same adversarial background knowledge. In practice, different users have different privacy requirements. Such non‐personalized privacy assumption does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. We study the personalized trajectory k‐anonymity criterion for trajectory data publication. Specifically, we explore and propose an overall framework which provides privacy preserving services based on users' personal privacy requests, including trajectory clustering, editing and publication. We demonstrate the efficiency and effectiveness of our scheme through experiments on real world dataset.