关键词: 隐私保护, 数据挖掘, 数据扰动, 非负矩阵分解, 样本选择, 分类

Abstract: In existing privacy-preserving data mining method based on NMF（Non-Negative Matrix Factorization）, every sample is equally important, and is perturbed with same degree. For solving this problem, a new method using sample selection and based on NMF is proposed. This method uses sample selection to divide samples into two parts: the important samples and the unimportant samples. Both important and unimportant samples are perturbed by using the existing NMF-based method. And then, unimportant samples are perturbed additionally by using the clustering properties of the NMF. The experiments show that, when keeping data utility, this new method can protect privacy well.

Key words: privacy protection, data mining, data perturbation, non-negative matrix factorization, sample selection, classification