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, poses the question of how to preserve the confidentiality and integrity of the outsourced data/computation. In this seminar, we discuss how each of these issues can be solved. First, we present a privacy-preserving collaborative filtering algorithm based on the well known slope-one predictor. We also discuss practical experiences from the engineering perspective when deploying privacy-preserving collaborative filtering on real world Software-as-a-Service enabling Platform-as- a-Service clouds. Finally, we present a verification mechanism for the outsourced collaborative filtering computation. We analyze the cheating behavior of the cloud from the game-theoretic perspective, and show how to make the computation incentive compatible, thus ensuring that a rational adversary will not cheat. Leveraging this, we can develop efficient and effective mechanisms to address the problem of integrity in outsourcing.