A Location-Privacy Approach for Continuous Queries
With the prevalence of smartphones, mobile apps have become more and more popular. However, many mobile apps request location information of the user. If there is nothing in place for location privacy, these mobile app users are in great risk of being tracked by malicious parties. Although the location privacy problem has been studied extensively by resorting to a third-party location anonymizer, there is very little work that allows the users to fully control the disclosure of their data using their smartphones alone. In this paper, we propose a novel Android App called MoveWithMe which automatically generates mocking locations. Most importantly, these mocking locations are not random like those generated by original Android location mocking function. The proposed MoveWithMe app generates k traces of mocking locations and ensures that each trace looks like a trace of a real human and each trace is semantically different from the real user’s trace.
D. Steiert et al., "A Location-Privacy Approach for Continuous Queries," Proceedings of the 22nd ACM Symposium on Access Control Models and Technologies (2017, Indianapolis, IN), pp. 115-117, Association for Computing Machinery (ACM), Jun 2017.
The definitive version is available at https://doi.org/10.1145/3078861.3084161
22nd ACM Symposium on Access Control Models and Technologies, SACMAT 2017 (2017: Jun. 21-23, Indianapolis, IN)
Intelligent Systems Center
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