Feeling-based location privacy protection for location-based services

Abstract

Anonymous location information may be correlated with restricted spaces such as home and office for subject re-identification. This makes it a great challenge to provide location privacy protection for users of location-based services. Existing work adopts traditional K-anonymity model and ensures that each location disclosed in service requests is a spatial region that has been visited by at least K users. This strategy requires a user to specify an appropriate value of K in order to achieve a desired level of privacy protection. This is problematic because privacy is about feeling, and it is awkward for one to scale her feeling using a number. In this paper, we propose a feeling-based privacy model. The model allows a user to express her privacy requirement by specifying a public region, which the user would feel comfortable if the region is reported as her location. The popularity of the public region, measured using entropy based on its visitors' footprints inside it, is then used as the user's desired level of privacy protection. With this model in place, we present a novel technique that allows a user's location information to be reported as accurate as possible while providing her sufficient location privacy protection. The new technique supports trajectory cloaking and can be used in application scenarios where a user needs to make frequent location updates along a trajectory that cannot be predicted. In addition to evaluating the effectiveness of the proposed technique under various conditions through simulation, we have also implemented an experimental system for location privacy-aware uses of location-based services.