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

Guang Li | Zendy; Yadong Wang | Zendy

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

Author(s) -

Guang Li,

Yadong Wang

Publication year - 2011

Publication title -

data science journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.358

H-Index - 21

ISSN - 1683-1470

DOI - 10.2481/dsj.009-025

Subject(s) - computer science , scope (computer science) , usability , data publishing , implementation , data science , metadata , transparency (behavior) , component (thermodynamics) , open data , reuse , world wide web , publishing , software engineering , computer security , political science , engineering , physics , human–computer interaction , law , waste management , thermodynamics , programming language

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

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