A Privacy-Preserving Framework for Personalized, Social Recommendations
Zach Jorgensen, Ting Yu · 2014
We consider the problem of producing item recommenda-tions that are personalized based on a user’s social network, while simultaneously preventing the disclosure of sensitive user-item preferences (e.g., product purchases, ad clicks, web browsing history, etc.). Our main contribution is a privacy-preserving framework for a class of social recommendation algorithms that provides strong, formal privacy guarantees under the model of differential privacy. Existing mechanisms for achieving differential privacy lead to an unacceptable loss of utility when applied to the social recommendation prob-lem. To address this, the proposed framework incorporates a clustering procedure that groups users according to the natural community structure of the social network and sig-nificantly reduces the amount of noise required to satisfy differential privacy. Although this reduction in noise comes at the cost of some approximation error, we show that the benefits of the former significantly outweigh the latter. We explore the privacy-utility trade-off for several different in-stantiations of the proposed framework on two real-world data sets and show that useful social recommendations can be produced without sacrificing privacy. We also experimen-tally compare the proposed framework with several existing differential privacy mechanisms and show that the proposed framework significantly outperforms all of them in this set-ting. 1.