Privacy-preserving collaborative filtering on the cloud and practical implementation experiences

Recommender systems typically use collaborative filtering to make sense of huge and growing volumes of data. An emerging trend in industry has been to use public clouds to deal with the computing and storage requirements of such systems. This, however, comes at a price-data privacy. Simply ensuring communication privacy does not protect against insider threats or even attacks agagainst the cloud infrastructure itself. To deal with this, several privacy-preserving collaborative filtering algorithms have been developed in prior research. However, these have only been theoretically analyzed for the most part. In this paper, we analyze an existing privacy preserving collaborative filtering algorithm from an engineering perspective, and discuss our practical experiences with implementing and deploying privacy-preserving collaborative filtering on real world Software-as-a-Service enabling Platform-as-a-Service clouds.