Survey of Privacy-Preserving Collaborative Filtering

TL;DR

This survey reviews recent advances in privacy-preserving collaborative filtering, highlighting methods that balance recommendation accuracy with user data privacy through various approaches and addressing different vulnerabilities.

Contribution

It provides a comprehensive classification of privacy-preserving techniques in collaborative filtering based on vulnerability types and solution approaches.

Findings

Classifies key methods by vulnerability and approach

Highlights trade-offs between privacy and recommendation quality

Identifies gaps and future directions in the field

Abstract

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.