Enhancing privacy and preserving accuracy of a distributed collaborative filtering

Abstract

Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to the recommendation process. Recent researches proposed to protect the privacy of CF by distributing the profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. This work investigates how a decentralized distributed storage of user profiles combined with data modification techniques may mitigate some privacy issues. Results of experimental evaluation show that parts of the user profiles can be modified without hampering the accuracy of CF predictions. The experiments also indicate which parts of the user profiles are most useful for generating accurate CF predictions, while their exposure still keeps the essential privacy of the users.