Private location centric profiles for GeoSocial networks
Bogdan Cărbunar, Mahmudur Rahman, Naphtali David Rishe, Jaime Ballesteros · 2012
Providing input to targeted advertising, profiling social network users is an important source of revenue for geosocial networks. Since profiles contain personal information, their construction introduces a trade-off between user privacy and incentives of participation for businesses and geosocial network providers. In this paper we introduce location centric profiles (LCPs), aggregates built over the profiles of users present at a given location. We introduce ProfilR, a suite of mechanisms that construct LCPs in a private and correct manner. Our Android implementation shows that ProfilR is efficient: the end-to-end overhead is small even under strong correctness assurances.