Data collection with self-enforcing privacy
Proceedings of the 13th ACM conference on Computer and communications security
On the leakage of personally identifiable information via online social networks
ACM SIGCOMM Computer Communication Review
| Hi-index | 0.00 |
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.