AGENT: an adaptive geo-indistinguishable mechanism for continuous location-based service

With the widespread use of Location-base Services(LBSs), the problem of location privacy has drawn significant attention from the research community. To protect the user’s exact location, a new notion of privacy, named geo-indistinguishability, that adapts differential privacy has been proposed for LBSs, recently. However, the obfuscation mechanism satisfying this privacy notion only works well in the case of snapshot LBS, which would not apply to the case of continuous LBSs due to the quick loss of privacy caused by the correlation between locations in the trace. In this paper, we propose a novel mechanism, namely AGENT, to protect the user’s location privacy in continuous LBSs. In AGENT, a R-tree is introduced to realize the reusable of generated sanitized locations, which achieves the notion of geo-indistinguishability with less consumption of privacy budget. Finally, empirical results over real-world dataset demonstrate that with the same utility, our mechanism consumes less privacy budget to obfuscate the same trace.