PROFILR Toward Preserving Privacy and Functionality in Geosocial Networks

PROFILR: Toward Preserving Privacy and Functionality in Geosocial Networks

Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacyvulnerabilities. In this paper, we propose to take first steps toward addressing the conflict between profit and privacy in geosocial networks. We introduce PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations (i.e., venues). PROFILR endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of colocated users. An Android implementation shows thatPROFILR is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.

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