Scalable and Secure Logistic Regression via Homomorphic Encryption
Yoshinori Aono, Takuya Hayashi, Le Trieu Phong, Lihua Wang · 2016
Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.