Achieving Location Privacy through CAST in Location Based Services

The widespread usage of location based services (LBS); where obtaining any informational service is entirely based upon the user's current location, have raised a significant concern about location privacy of the user. For the queries like where is my closest ATM?, where is my nearest hospital, or any route assistance in general, it is essential to submit the user's actual location to avail the demanded services. Similar communication holds true for a location based vehicular transportation system. Cloaking and ob-fuscation are the two generalized approaches to deal with location privacy preservation in LBS. These approaches are mainly based on a trusted third party (TTP) and exploits the well established K-anonymity principle in order to make the query issuer indistinguishable with other K - 1 more users. In such approaches all the data (mainly location coordinates and queries) becomes available at the central server, thus complete knowledge of the query (including user id) exists at central node. This is the major limitation of TTP based architecture and makes such frameworks susceptible to different privacy attacks. This paper is a research attempt to extend the realm of collaborative communication among peers belonging to a mobile user group in a decentralized or trusted third party free architecture. We propose a collaborative P2P communication model; called CAST, that employs the series of trust among peers and peers use their cached mobile data to collaborate with each other in order to get the results locally. The scheme provides results locally with low latency and works efficiently when the peers share common inclinations (or data value). The proposed algorithm preserves user's privacy and performs effectively under pull-based sporadic query scenario.