Privacy-preserving content-based recommender system

Abstract

By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely, also poses a serious privacy risk for the customers: ratings may be very personal or revealing, and thus highly privacy sensitive. Service providers could process the collected rating data for other purposes, sell them to third parties or fail to provide adequate physical security. In this paper, we propose technological mechanisms to protect the privacy of individuals in a recommender system. Our proposal is founded on homomorphic encryption, which is used to obscure the private rating information of the customers from the service provider. While the user's privacy is respected by the service provider, by generating recommendations using encrypted customer ratings, the service provider's commercially valuable item-item similarities are protected against curious entities, in turn. Our proposal explores simple and efficient cryptographic techniques to generate private recommendations using a server-client model, which neither relies on (trusted) third parties, nor requires interaction with peer users. The main strength of our contribution lies in providing a highly efficient solution without resorting to unrealistic assumptions.