Eat the Cake and Have It Too: Privacy Preserving Location Aggregates in Geosocial Networks
This addresses privacy concerns for users in geosocial networks while maintaining business incentives, though it appears incremental as it builds on existing privacy-preserving aggregation concepts.
The paper tackles the privacy-incentive trade-off in geosocial networks by introducing location centric profiles (LCPs) and PROFILR, a suite of mechanisms that construct these aggregates privately and correctly, with implementations showing small end-to-end overhead.
Geosocial networks are online social networks centered on the locations of subscribers and businesses. Providing input to targeted advertising, profiling social network users becomes an important source of revenue. Its natural reliance on personal information 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. We introduce iSafe, a novel, context aware public safety application built on PROFILR . Our Android and browser plugin implementations show that PROFILR is efficient: the end-to-end overhead is small even under strong correctness assurances.