Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2018
Abstract
Vehicular Ad hoc Networks (VANETs) have higher requirements of continuous Location-Based Services (LBSs). However, the untrusted server could reveal the users’ location privacy in the meantime. Syntactic-based privacy models have been widely used in most of the existing location privacy protection schemes. Whereas, they are suffering from background knowledge attacks, neither do they take the continuous time stamps into account. Therefore we propose a new differential privacy definition in the context of location protection for the VANETs, and we designed an obfuscation mechanism so that fine-grained locations and trajectories will not exposed when vehicles request location-based services on continuous time stamps. Then, we apply the exponential mechanism in the pseudonym permutations to provide disparate pseudonyms for different vehicles when making requests on different time stamps, these pseudonyms can hide the position correlation of vehicles on consecutive time stamps besides releasing them in a coarse-grained form simultaneously. The experimental results on real-world datasets indicate that our scheme significantly outperforms the baseline approaches in data utility.
Keywords
LBS, VANETs, Location privacy, Continuous time stamps, Differential privacy
Discipline
Theory and Algorithms
Publication
Proceedings of the 18th International Conference, ICA3PP 2018, Guangzhou, China, November 15-17
Volume
11337 LNCS
First Page
204
Last Page
219
ISBN
9783030050627
Identifier
10.1007/978-3-030-05063-4_17
Publisher
Springer
City or Country
Cham
Citation
CHEN, Zhili; BAO, Xianyue; YING, Zuobin; LIU, Ximeng; and ZHONG, Hong.
Differentially private location protection with continuous time stamps for VANETs. (2018). Proceedings of the 18th International Conference, ICA3PP 2018, Guangzhou, China, November 15-17. 11337 LNCS, 204-219.
Available at: https://ink.library.smu.edu.sg/sis_research/10202
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-05063-4_17