PRIVACY PRESERVATION IN LOCATION-BASED PROXIMITY SERVICES

Abstract

One of the most common location-based services (LBS) in the geo-aware social network market is the notification of friends geographically in proximity. In addition to the privacy threats related to the use of traditional LBS, there are other privacy threats specific to proximity services. Existing privacy-preserving solutions for LBS are not effective or directly applicable. For this reason, we developed techniques that specifically address the privacy threats of this type of services. The proposed techniques let a user control what is disclosed about her location and formally guarantee that these requirements are satisfied. An extensive empirical evaluation was performed, by using a dataset of user movement generated using an agent-based simulator, in which agents reflect the behavior of typical users of proximity services. The techniques were also integrated in a fully functional privacy-aware proximity service, for which we developed desktop and mobile clients.