As e-commerce websites began to develop, a pressing need emerged for providing recommendations derived from filtering the whole range of available alternatives. Users were finding it very difficult to make the most appropriate choices from the immense variety of items (products and services) that these websites were offering. Take an online book store as an example, going through the lengthy book catalogue would not only waste a lot of time but also frequently overwhelm users and lead them to make poor decisions. As such, the availability of choices, instead of producing a benefit, started to decrease users’ well-being. Eventually, this need led to the development of recommender systems (or, recommendation systems). Informally, recommender systems are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that a user would give to an item (such as music, book, or movie) or social element (e.g. people or group) they had not yet considered, using a model built from the characteristics of items and/or users. In order to compute recommendations for users, a recommendation service provider needs to collect a lot of personal data from its customers, such as ratings, transaction history, and location. This makes recommender systems a double-edged sword. On one side users get better recommendations when they reveal more personal data, but on the flip side they sacrifice more privacy if they do so. In this project, we aim at solve the utility-privacy dilemma, namely we want to protect users’ privacy to the maximal extent while still enable them to receive accurate recommendations. We will investigate the realistic privacy notions for recommender systems, and invent privacy-enhancing technologies that allow recommendations to be generated in a secure manner (e.g. generated on encrypted data). We expect that the resulting technologies can also be used in other related services, e.g. privacy-preserving event correlation between different ISPs (Internet Service Providers).