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

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概要

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• 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.

• CODATAの論文

著者

• Li Guang

  School of Computer Science and Technology, Harbin Institute of Technology

• Wang Yadong

  School of Computer Science and Technology, Harbin Institute of Technology

関連論文

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

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