Differentially Private Location Protection with Continuous Time Stamps for VANETs
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.
KeywordsLBS VANETs Location privacy Continuous time stamps Differential privacy
The work is supported by the Natural Science Foundation of China under Grant No. 61572031 & U1405255. We thank the anonymous reviewers for their valuable comments that helped improve the final version of this paper.
- 1.Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 901–914. ACM (2013)Google Scholar
- 3.Cui, J., Wen, J., Han, S., Zhong, H.: Efficient privacy-preserving scheme for real-time location data in vehicular ad-hoc network. IEEE Internet Things J. (2018)Google Scholar
- 7.Ghinita, G., Kalnis, P., Khoshgozaran, A., Shahabi, C., Tan, K.L.: Private queries in location based services: anonymizers are not necessary. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 121–132. ACM (2008)Google Scholar
- 8.Jorgensen, Z., Yu, T., Cormode, G.: Conservative or liberal? Personalized differential privacy. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 1023–1034. IEEE (2015)Google Scholar
- 11.McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 94–103. IEEE (2007)Google Scholar
- 15.Pan, X., Meng, X., Xu, J.: Distortion-based anonymity for continuous queries in location-based mobile services. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 256–265. ACM (2009)Google Scholar
- 17.Shin, H., Vaidya, J., Atluri, V., Choi, S.: Ensuring privacy and security for LBS through trajectory partitioning. In: 2010 Eleventh International Conference on Mobile Data Management (MDM), pp. 224–226. IEEE (2010)Google Scholar
- 18.Xiao, Y., Xiong, L.: Protecting locations with differential privacy under temporal correlations. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1298–1309. ACM (2015)Google Scholar
- 23.Zheng, Y.: T-drive trajectory data sample, August 2011. https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/