Methods of privacy preserving in collaborative filtering

Privacy considerations of individuals becomes more and more popular issue in recommender systems due to the increasing need for protecting confidential data. Even though users of recommender systems enjoy with personalized productions, they behave timidly about sharing their private data due to the some privacy concerns about price discrimination, unsolicited marketing, govern-ment surveillance and etc. Thus, preserving confidential data of users while producing accurate predictions is one of the extremely important directions of the researches about recommendation systems. In this paper, we gather the most known studies and recently published ones about producing accurately predictions without endangering privacy in order to guide researchers interested with privacy concerns in recommender systems. Moreover, we give a brief discussion about utilized methods.

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