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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Veröffentlicht in:	IEICE Transactions on Information and Systems 2016/08/01, Vol.E99.D(8), pp.2079-2089
Hauptverfasser:	AONO, Yoshinori, HAYASHI, Takuya, PHONG, Le Trieu, WANG, Lihua
Format:	Artikel
Sprache:	eng
Schlagworte:	accuracy distributed data sources F-score homomorphic encryption logistic regression LWE outsourced computation Paillier ring-LWE
Online-Zugang:	Volltext
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