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In today's Internet, recommender systems play an indispensable role in helping users discover items of interests, such as products, books, movies and so on. However, a higher recommendation accuracy is commonly at the cost of more disclosure of user privacy. Thus, a wider adoption of recommender systems poses significant security and privacy concerns to users. In this article, we propose a light-weight privacy-preserving framework called RAP for recommender systems, which can protect user privacy while still ensuring a high recommendation accuracy. Instead of directly sending users' private ratings to the recommender, users first conduct a local perturbation operation on private ratings, and then send the perturbed ratings to the recommender. The recommender can run recommendation algorithms directly over the perturbed ratings and return the results to users. Different from crypto-based methods, our perturbation and de-perturbation methods are linear operations. Thus, RAP is light-weight and highly efficient in privacy protection. To be more rigorous, we formally prove that the order of recommendation accuracy will not decrease when our RAP framework is applied to any MF (Matrix Factorization)-based recommender systems. We also derive the closed-form expression for the degree of privacy preservation of our framework. Finally, we conduct extensive evaluations using large-scale real-world datasets to verify the effectiveness of our RAP framework and compare with other baseline algorithms. The results show that our RAP framework can improve the degree of privacy preservation from zero to over 0.5 for the Movielens dataset and 4 for the Jester dataset, and still maintain the approaching level of recommendation accuracy.
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