Automated recommender systems are used to help people find interesting content or persons in the vast amount of information available via the internet. There are different types of recommender systems, for example collaborative filtering systems and content-based recommender systems. However, all recommender systems share a common trait: in order to generate personalized recommendations, they require information on the attributes, demands, or preferences of the user. Typically, the more detailed the information related to the user is, the more accurate the recommendations for the user are. Service providers running the recommender systems collect large amounts of personal information to ensure accurate recommendations. This data must be protected to increase the privacy of all participating users.\ud \ud Privacy is typically enhanced through one (or more) of three methods: (1) decentralization, (2) introduction of uncertainty, and (3) secure computation.\ud \ud Decentralization aims to remove the central service provider and gives more control to the individual users. However, decentralized systems cannot guarantee the availability of data as users go online and offline as they please. Furthermore, no single entity is responsible for data that does not belong to a specific user (such as item data).\ud \ud Uncertainty is typically introduced by adding random noise to the data, which provides a mask over the user information. However, this noise negatively impacts the accuracy of the recommender system. When the users introduce their own noise, then the system consists mainly of noise. To preserve accuracy, only the service provider introduces noise, therefore no privacy is achieved against the service provider.\ud \ud Secure computation protects the data that is used during the computation of recommendations by providing confidentiality, both at rest and during computation. However, it suffers from a large computational overhead, due to the use of cryptography and secure multi-party protocols.\ud \ud In this thesis we focus on the use of secure computation to enhance the privacy of recommender systems, where we strive to make the computations as efficient as possible. To provide this, we build specialized secure computation protocols based on homomorphic encryption schemes and secure multi-party computation
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