Integrating identity, location, and absence privacy in context-aware retrieval of points of interest

The retrieval of close-by points of interest (POIs) is becoming a popular location-based service (LBS), often integrated with navigational services and geo-social networks. However, the access to POI services is prone to potentially serious privacy issues, since requests for POIs often include sensitive information like the user's location and her personal interests. Many techniques to enforce privacy in LBS have been proposed in the literature, in some cases focusing on anonymizing the requests and in others on obfuscating information in order to decrease its sensitivity. In many cases privacy protection comes at some cost in terms of service precision and performance. In this paper we propose a novel technique that combines the above cited approaches, overcomes some of their limitations in terms of assumptions on adversary knowledge, while still guaranteeing service precision. Our privacy solution has been integrated in an existing distributed system to share and retrieve POIs based not only on the user's current location but also on other (possibly sensitive) context data.