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Research Papers

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

Authors:

Guang Li ,

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
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Yadong Wang

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
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Abstract

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.
DOI: http://doi.org/10.2481/dsj.009-025
How to Cite: Li, G. & Wang, Y., (2011). A Privacy-Preserving Data Mining Method Based on Singular Value Decomposition and Independent Component Analysis. Data Science Journal. 9, pp.124–132. DOI: http://doi.org/10.2481/dsj.009-025
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Published on 08 Feb 2011.
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