MoveWithMe: Location Privacy Preservation for Smartphone Users
With the prevalence of smartphones, mobile websites have been more and more popular. However, many mobile websites collect the location information which greatly increases users' risks of being tracked unexpectedly. The current location access control setting is not sufficient since it cannot prevent the service providers which have been granted location-access permissions from tracking the users. In this paper, we propose a novel location privacy preservation mobile app, called MoveWithMe, which automatically generates decoy queries to hide the real users' locations and intentions when they are using location-based mobile services. Unlike the existing works on dummy trajectories which may be easily discovered by attackers through data analysis, the uniqueness of the MoveWithMe app is that our generated decoys closely behave like real humans. Each decoy in our system has its own moving patterns, daily schedules, social behaviors, etc., which ensures its movements to be semantically different from the real user's trace as well as satisfying geographic constraints. Thus, our decoys can hardly be distinguished even by advanced data mining techniques. Another advantage of the MoveWithMe app is that it guarantees the same level of user experience without affecting the response time or introducing extra control burdens. Decoys move independently in the back end and automatically submit queries to the same service provider whenever the user does so. Our proposed MoveWithMe app has both iOS and Android versions and has been tested on different brands of smartphones against various location-based services such as Yelp and TripAdvisor. The experimental results demonstrate its practicality, effectiveness and efficiency.
J. Kang et al., "MoveWithMe: Location Privacy Preservation for Smartphone Users," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 711-724, Institute of Electrical and Electronics Engineers (IEEE), Jul 2019.
The definitive version is available at https://doi.org/10.1109/TIFS.2019.2928205
Keywords and Phrases
Computer science; Data mining; Encryption; Location Privacy; Location-based Service; Mobile App; Privacy; Smartphone; Trajectory; Urban areas
International Standard Serial Number (ISSN)
Article - Journal
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