Privacy Preserving in Collaborative Filtering Based Recommender System: A Systematic Literature Review

摘要

Recommender systems solve the information overload problem by filtering data on the basis of the user's preferences, interest, or previous behavior regarding an item. Data filtering techniques employed are content-based (based on the user's past behavior), collaborative (based on the behavior of users that are alike to the active one), or hybrid (a combination of filtering techniques). Due to its versatility, the most popular technique used in the recommender systems is collaborative filtering. However, the privacy of the user is at risk because malicious users can attack the targeted user or the recommendation server may reveal the personal data of users' to other parties or misuse the data for targeted advertising. The existing works mostly employ encryption or randomizations based methodologies, but often sacrifice privacy for accuracy and accuracy for privacy.