Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption

Homomorphic encryption (HE) is one of promising cryptographic candidates resolving privacy issues in machine learning on sensitive data such as biomedical data and financial data. However, HE-based solutions commonly suffer from relatively high computational costs due to a large number of iterations...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Jung Hee Cheon, Duhyeong Kim, Yongdai Kim, Yongsoo Song
Format:	Article
Language:	English
Published:	IEEE 2018-01-01
Subjects:	Ensemble gradient descent with errors homomorphic encryption privacy-preserving logistic regression
Online Access:	https://ieeexplore.ieee.org/document/8444365/