New SVD-based collaborative filtering algorithms with differential privacy

In the era of big data, real and reliable user data information is an important factor in the recommendation technology; therefore, the disclosure of personal privacy has become a significant problem user concern. Differential privacy protection is a proven and very strict privacy protection technology, which is particularly good at protecting the privacy of indirect derivation. Singular Value Decomposition (SVD) is one of the common matrix factorization techniques used in collaboration filtering for recommender systems and it considers the user and item bias. This paper will develop a flexible application that implements differential privacy in SVD. As part of the development process, on one hand, our algorithms do not need to perform any pre-processing of the raw input matrix. On the other hand, the experimental results, using two real datasets, show that our algorithms not only protect privacy information in the raw data but also ensure the accuracy of recommendations. Finally, a trade-off scheme is used, which can balance the privacy protection and the recommendation accuracy to a certain extent.