Personalized Location Privacy Protection Based on Vehicle Movement Regularity in Vehicular Networks
Hong Zhong, Jingyue Ni, Jie Cui, Jing Zhang, Lu Liu
Abstract
Currently, the issue of location privacy has been attracting increasing attention. In traditional pseudonym-based solutions, the pseudonym age of each vehicle increases at the same rate and vehicles change their pseudonyms when they meet vehicles, which also want to change pseudonyms at the same location. However, we observe that according to the individual characteristics of each vehicle’s owner, the movement of each vehicle is regular, i.e., there are some locations that vehicles visit frequently, and some locations that they rarely visit. Evidently, pseudonym change strategies for these locations are different. To address these issues, we propose a sensitivity-based pseudonym change mechanism, which can take full advantage of the regularity of a vehicle’s movement, thereby achieving personalized location privacy. A pseudonym age analytical model is used to quantify the achieved location privacy. Furthermore, we propose a new metric to measure the level of personalized location privacy protection. The results of the performance evaluation demonstrate that our approach significantly outperforms existing approaches in realizing personalized location privacy.