Randomization-based Privacy-preserving Frameworks for Collaborative Filtering

Abstract

Randomization-based privacy protection methods are widely used in collaborative filtering systems to achieve individual privacy. The basic idea behind randomization utilized in collaborative filtering schemes is to add randomness to original data in such a way so that required levels of accuracy and privacy can be achieved. Although there are various studies on privacy-preserving collaborative filtering using randomization, there are no well-defined privacy-preserving frameworks for collaborative filtering algorithms based on randomization. In this paper, we present eight randomization-based privacy-preserving frameworks for privacy protection in collaborative filtering schemes. We first group privacy-preserving methods into two broad categories. We then classify them based on private data. Final grouping is done while considering varying privacy concerns of individual users. The frameworks can be chosen according to individual users' expectations and be utilized for privacy protection. The well-defined privacy-preserving frameworks form a basis for privacy protection based on randomized perturbation and randomized response techniques in collaborative filtering studies.