A Privacy-Preserving Data Mining Method Based on Singular Value Decomposition and Independent Component Analysis

Data Science Journal

Guang Li¹  Yadong Wang¹ 

[1] School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China;

关键词: Privacy preservation;    Data mining;    Singular value decomposition;    Independent component analysis;   

DOI  :  10.2481/dsj.009-025

来源: DOAJ

【 摘 要 】

Privacy protection is indispensable in data mining, and many privacy-preserving data mining (PPDM) methods have been proposed. One such method is based on singular value decomposition (SVD), which uses SVD to find unimportant information for data mining and removes it to protect privacy. Independent component analysis (ICA) is another data analysis method. If both SVD and ICA are used, unimportant information can be extracted more comprehensively. Accordingly, this paper proposes a new PPDM method using both SVD and ICA. Experiments show that our method performs better in preserving privacy than the SVD-based methods while also maintaining data utility.

【 授权许可】

Unknown