Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy. The more information is revealed to the server on the user-item relations, the lower the users' privacy is. Yet, hiding part of the profiles to increase the privacy comes at the cost of recommendation accuracy or difficulty of implementing the method. In this paper, we propose a distributed mechanism for users to augment their profiles in a way that obfuscates the user-item connection to an untrusted server, with minimum loss on the accuracy of the recommender system. We rely on the central server to generate the recommendations. However, each user stores his profile offline, modifies it by partly merging it with the profile of similar users through direct contact with them, and only then periodically uploads his profile to the server. We propose a metric to measure privacy at the system level, using graph matching concepts. Applying our method to the Netflix prize dataset, we show the effectiveness of the algorithm in solving the tradeoff between privacy and accuracy in recommender systems in an applicable way.