  Collaborative filtering (CF) recommendation algorithms are well-known for
their outstanding recommendation performances, but previous researches showed
that they could cause privacy leakage for users due to k-nearest neighboring
(KNN) attacks. Recently, the notion of differential privacy (DP) has been
applied to privacy preservation for collaborative filtering recommendation
algorithms. However, as far as we know, existing differentially private CF
recommendation schemes degraded the recommendation performance (such as recall
and precision) to an unacceptable level. In this paper, in order to address the
performance degradation problem, we propose a differentially private user-based
collaborative filtering recommendation scheme based on k-means clustering
(KDPCF). Specifically, to improve the recommendation performance, we first
cluster the dataset into categories by k-means clustering and appropriately
adjust the size of the target category to which the target user belongs, so
that only users in the well-sized target category can be used for
recommendations. Then we efficiently select a set of neighbors from the target
category at one time by employing only one exponential mechanism instead of the
composition of multiple ones, and base on the neighbor set to recommend. We
theoretically prove that our scheme achieves differential privacy. Empirically,
we use the MovieLens dataset to evaluate our recommendation system. The
experimental results demonstrate a significant performance gain compared to
existing schemes.
