A Review towards Preserving Privacy and Functionality in Geosocial Networks

Profit is the main contribution reason for social network providers. Its dependence on user profiles, built from a wealth of freely revealed personal information, exposes users to a variety of privacy vulnerabilities. ONLINE social networks have become a significant source of personal information. In this paper, propose to take first steps toward addressing the difference between profit and privacy in geo social networks. This paper establish PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited separate locations . PROFILR endows users with strong privacy guarantees and providers with correctness assurance In accumulation to a venue centric advance, we propose a decentralized solution for computing real time LCP snapshots over the profiles of co-located users. An Android implementation shows that PROFILR is efficient; the continuous transparency is small even under strong privacy and correctness assurances.