Presentation 2013-07-18
Privacy-preserving Online Logistic Regression Based on Homomorphic Encryption
Shuang WU, Junpei KAWAMOTO, Hiroaki KIKUCHI, Jun SAKUMA,
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Abstract(in English) Preserve the privacy of personal information when conducting statistical analysis has attracted much attention in machine learning and data mining. In this work, we propose an approach to realize privacy-preserving logistic regression when data are held by different individuals--without actually combining the data together. In our approach, we use polynomial fitting to approximate the logistic function in order to solve the problem that logistic function is not available in the secure settings (encryptions are not applicable) because of its non-linear property. And the experiment shows that our approach achieves good prediction accuracy compared with original logistic regression.
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Keyword(in English) privacy-preserving / polynomial fitting
Paper # IBISML2013-10
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Committee IBISML
Conference Date 2013/7/11(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Privacy-preserving Online Logistic Regression Based on Homomorphic Encryption
Sub Title (in English)
Keyword(1) privacy-preserving
Keyword(2) polynomial fitting
1st Author's Name Shuang WU
1st Author's Affiliation Graduate School of SIE, University of Tsukuba()
2nd Author's Name Junpei KAWAMOTO
2nd Author's Affiliation Information and Systems, University of Tsukuba
3rd Author's Name Hiroaki KIKUCHI
3rd Author's Affiliation School of Interdisciplinary Mathematical Sciences, Meiji University
4th Author's Name Jun SAKUMA
4th Author's Affiliation Graduate School of SIE, University of Tsukuba
Date 2013-07-18
Paper # IBISML2013-10
Volume (vol) vol.113
Number (no) 139
Page pp.pp.-
#Pages 8
Date of Issue