Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation

• Published in: JMIR medical informatics Vol. 6; no. 2; p. e19
• Main Authors: Kim, Miran, Song, Yongsoo, Wang, Shuang, Xia, Yuhou, Jiang, Xiaoqian
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 17.04.2018
• Subjects: Approximation; Biomedical research; Efficiency; Machine learning; Methods; Neural networks; Original Paper; Outsourcing; Privacy; Regression analysis; homomorphic encryption; logistic regression; machine learning; gradient descent

Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Ho...