A practical k-anonymous recommender system

Providing users with a lot of information might sound ideal in many scenarios, nonetheless, this may often be very annoying for the end user. To limit the amount of information that has to be processed by the user, to a set that is more relevant for his needs, most service providers use recommender systems. Undoubtedly, to provide someone with proper recommendations, one needs some background knowledge about this individual. Nonetheless, depending on the nature of the query, the database and the means to acquire this background knowledge, a lot of sensitive user information can be leaked to the system, something that many companies try to monetize. To address such privacy issues, privacy preserving recommendation systems have been developed. In this work we introduce an efficient collaborative filtering system which uses state of the art encryption primitives to offer privacy to both the server and the client, while simultaneously providing quality recommendations to the latter.