Privacy preserving collaborative filtering for KNN attack resisting

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNNattack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering(PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCFcontains an essential privacy operation, Private Neighbor Selection, in which the Laplacenoise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivityand a re-designed truncated similarityare introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attackwhile retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCFalgorithm retains most of the utility with a fixed privacy budget.