With the fast adoption of location-enabled devices, Location-based Applications (LBAs) have become widely popular. While LBAs enable highly useful concepts such as geo-social networking, their use also raises serious privacy concerns as it involves sharing of location data with non-trusted third parties. In this respect, we propose an approach that protects the frequently visited locations of users, e.g., a bar, against inferences from longterm monitoring of their location data. Such inferences equate a privacy leak as they reveal a user’s personal behavior and interests to possibly malicious non-trusted parties.

To this end, we first present a study of a dataset of location check-ins to show the existence of this threat among users of LBAs. We then propose our approach to protect visit-frequency of the users to different locations by distributing their location data among multiple third-party Location Servers. This distribution not only serves to avoid a single point of failure for privacy in our system, it also allows the users to control which LBA accesses what information about them. We also describe a number of possible attacks against our privacy approach and evaluate them on real-data from the check-ins dataset. Our results show that our approach can effectively hide the frequent locations while supporting good quality-of-service for the LBAs.