Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhap...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E99.D; no. 8; pp. 2079 - 2089
Main Authors AONO, Yoshinori, HAYASHI, Takuya, PHONG, Le Trieu, WANG, Lihua
Format Journal Article
LanguageEnglish
Published The Institute of Electronics, Information and Communication Engineers 01.08.2016
Subjects
accuracy
distributed data sources
F-score
homomorphic encryption
logistic regression
LWE
outsourced computation
Paillier
ring-LWE
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Summary:Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.
ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.2015INP0020