Analyzing the conflict on privacy preserving mechanisms and functionality in geo social networks, PROFILR-A framework is proposed for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations thereby preserving users from unwanted issues. PROFIL Rendows users with strong privacy guarantees and providers with correctness assurances. Steps are taken toward addressing this conflict. The approach is based on the concept of location centric profiles (LCPs). LCPs are statistics built from the profiles of users that have visited a certain location or a set of co-located users. A novel approach is proposed to define the location and user based safety metrics. Our key insight is to apply secure user-specific, distance-preserving coordinate transformations to all location data shared with the server. In addition to a venue centric approach, a decentralized solution is proposed for computing LCP snapshots over the profiles of co-located users is presented for private information retrieval that allows a user to retrieve information. In future, cryptographic techniques are further applied to enhance the security such that a technique from a database server without revealing what is actually being retrieved from the server. This allows all location queries to be evaluated correctly by the server, but our privacy mechanisms guarantee that servers are unable to see or infer the actual location data from the transformed data or from the data access.

Keywords—ProfilR; Location Centric Profiles; Geo-aware social networks. ;