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

Yoshinori AONO  Takuya HAYASHI  Le Trieu PHONG  Lihua WANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.8   pp.2079-2089
Publication Date: 2016/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015INP0020
Type of Manuscript: Special Section PAPER (Special Section on Security, Privacy and Anonymity of Internet of Things)
Category: 
Keyword: 
logistic regression,  distributed data sources,  homomorphic encryption,  Paillier,  LWE,  ring-LWE,  outsourced computation,  accuracy,  F-score,  

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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.