A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

Autor:	Zhijian Shao, Bingwen Feng, Xingzheng Li
Rok vydání:	2021
Předmět:	Service (systems architecture) Markov chain Point (typography) Computer science business.industry Computer security computer.software_genre Data set Obfuscation Trajectory Selection (linguistics) The Internet business computer
Zdroj:	Journal of Computer and Communications. :35-56
ISSN:	2327-5227 2327-5219
DOI:	10.4236/jcc.2021.94003
Popis:	Trajectory data set is the indispensable foundation for constructing reliable Internet of Vehicles (IoV) service and location-based service (LBS), while it is likely to be abused by malicious attackers to infer user’s privacy. In this paper, we propose a trajectory protection method based on stop points obfuscation, which can confront various privacy attacks and preserve the semantic information to achieve adequate utility. Two strategies for stop point selection are designed, including category-distance priority method and Markov matrix method. Our new method was analyzed and evaluated on a real-world trajectory data set. The experiment result shows that our method can improve the utility of the data set and provide multi-level privacy protection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a0deadd93dd51bc788071a482df99403 https://doi.org/10.4236/jcc.2021.94003 Zobrazit plný text záznamu