Personalized Social Recommendations - Accurate or Private?

With the recent surge of social networks such as Facebook, new forms of recommendations have become possible – recommendations that rely on one’s social connections in order to make personalized recommendations of ads, content, products, and people. Since recommendations may use sensitive information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing trade-offs between accuracy and privacy of personalized social recommendations. We study whether “social recommendations”, or recommendations that are solely based on a user’s social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We ...

• Type: Journal
• Year: 2011
• Where: PVLDB
• Authors: Ashwin Machanavajjhala, Aleksandra Korolova, Atish Das Sarma
• Keywords: Computer Networks, Privacy, PVLDB 2011, Social, Social Recommendations