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Most industrial recommender systems rely on the popular collaborativefiltering (CF) technique for providing personalized recommendations to itsusers. However, the very nature of CF is adversarial to the idea of userprivacy, because users need to share their preferences with others in order tobe grouped with like-minded people and receive accurate recommendations. Whileprevious privacy preserving approaches have been successful inasmuch as theyconcealed user preference information to some extent from a centralizedrecommender system, they have also, nevertheless, incurred significanttrade-offs in terms of privacy, scalability, and accuracy. They are alsovulnerable to privacy breaches by malicious actors. In light of theseobservations, we propose a novel selective privacy preserving (SP2) paradigmthat allows users to custom define the scope and extent of their individualprivacies, by marking their personal ratings as either public (which can beshared) or private (which are never shared and stored only on the user device).Our SP2 framework works in two steps: (i) First, it builds an initialrecommendation model based on the sum of all public ratings that have beenshared by users and (ii) then, this public model is fine-tuned on each user'sdevice based on the user private ratings, thus eventually learning a moreaccurate model. Furthermore, in this work, we introduce three differentalgorithms for implementing an end-to-end SP2 framework that can scaleeffectively from thousands to hundreds of millions of items. Our user surveyshows that an overwhelming fraction of users are likely to rate much more itemsto improve the overall recommendations when they can control what ratings willbe publicly shared with others.

How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems