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Recommender systems (RSs) completely rely on the knowledge of training information to generate recommendations. However, due to privacy, ownership, and protection of users’ information, such training information is not easily accessible or shared with a Recommender system. We will outline our research conducted at MTC ETH Zurich that aims to learn a high-quality recommendation model without ever directly accessing raw training data and present the results obtained from our experiments.