Personalized Social Recommendations - Accurate or Private?

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 lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to the lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.