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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | privacy-preserving / polynomial fitting |
Paper # | IBISML2013-10 |
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Committee | IBISML |
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Conference Date | 2013/7/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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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 |
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