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.

中文翻译：

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隐私保护协同过滤调查

协同过滤推荐系统根据用户过去的偏好以及具有相似兴趣的其他用户的偏好向用户提供推荐。近年来，推荐系统的使用得到了广泛的发展，帮助人们选择要观看的电影、阅读的书籍和购买的物品。然而，用户在使用此类系统时往往会担心他们的隐私，许多用户不愿意向大多数在线服务提供准确的信息。隐私保护的协同过滤推荐系统旨在为用户提供准确的推荐，同时保持对其数据隐私的某些保证。本次调查研究了隐私保护协同过滤方面的最新文献，