IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Special Section on Security, Privacy and Anonymity of Internet of Things
Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption
Yoshinori AONOTakuya HAYASHILe Trieu PHONGLihua WANG
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2016 Volume E99.D Issue 8 Pages 2079-2089

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Abstract

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.

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© 2016 The Institute of Electronics, Information and Communication Engineers
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