Personalized Social Recommendations - Accurate or Private?
Ashwin Machanavajjhala (Yahoo! Research), Aleksandra Korolova, (Stanford University), Atish Das Sarma (Google)

TL;DR
This paper investigates the trade-offs between privacy and accuracy in social recommendations, formalizing the privacy-utility balance and analyzing the feasibility of differentially private algorithms in social networks.
Contribution
It formalizes privacy-utility trade-offs in social recommendations and evaluates the performance of differentially private algorithms both theoretically and experimentally.
Findings
Good private recommendations are feasible only for a small subset of users.
There is a significant utility loss when enforcing strong privacy guarantees.
Lower bounds on utility loss are established for differentially private recommendation algorithms.
Abstract
With the recent surge of social networks like Facebook, new forms of recommendations have become possible - personalized recommendations of ads, content, and even new friend and product connections based on one's social interactions. Since recommendations may use sensitive social information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing these expected trade-offs between the accuracy and privacy of personalized social recommendations. In this paper, 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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Mobile Crowdsensing and Crowdsourcing
