Differentially private user-based collaborative filtering recommendation based on κ-means clustering

Collaborative filtering (CF) recommendation is well-known for its outstanding recommendation performance, but previous researches showed that it could cause privacy leakage for users due to kappa-nearest neighboring (KNN) attacks. Recently, the notion of differential privacy (DP) has been applied to privacy preservation in recommendation systems. However, as far as we know, existing differentially private CF recommendation systems degrade the recommendation performance (such as recall and precision) to an unacceptable level. In this paper, to address the performance degradation problem, we propose a differentially private user-based CF recommendation system based on kappa-means clustering (KDPCF). Specifically, to improve the recommendation performance, KDPCF first clusters the dataset into categories by kappa-means clustering and appropriately adjusts the size of the target category to which the target user belongs, so that only users in the well-sized target category are used for recommendation. Then, it selects efficiently a set of neighbors from the target category at one time by employing only one instance of exponential mechanism instead of the composition of multiple ones, and then uses a CF algorithm to recommend based on this set of neighbors. We theoretically prove that our system achieves differential privacy. Empirically, we use two public datasets to evaluate our recommendation system. The experimental results demonstrate that our system has a significant performance improvement compared to existing ones.