A privacy-preserving framework for personalized, social recommendations

We consider the problem of producing item recommendations 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 privacypreserving 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 problem. To address this, the proposed framework incorporates a clustering procedure that groups users according to the natural community structure of the social network and significantly 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 instantiations of the proposed framework on two real-world data sets and show that useful social recommendations can be produced without sacrificing privacy. We also experimentally compare the proposed framework with several existing differential privacy mechanisms and show that the proposed framework significantly outperforms all of them in this setting.