  Collaborative filtering recommendation systems provide recommendations to
users based on their own past preferences, as well as those of other users who
share similar interests. The use of recommendation systems has grown widely in
recent years, helping people choose which movies to watch, books to read, and
items to buy. However, users are often concerned about their privacy when using
such systems, and many users are reluctant to provide accurate information to
most online services. Privacy-preserving collaborative filtering recommendation
systems aim to provide users with accurate recommendations while maintaining
certain guarantees about the privacy of their data. This survey examines the
recent literature in privacy-preserving collaborative filtering, providing a
broad perspective of the field and classifying the key contributions in the
literature using two different criteria: the type of vulnerability they address
and the type of approach they use to solve it.
