Scalable and Secure Logistic Regression via Homomorphic Encryption

@article{Aono2016ScalableAS,
  title={Scalable and Secure Logistic Regression via Homomorphic Encryption},
  author={Yoshinori Aono and Takuya Hayashi and Le Trieu Phong and Lihua Wang},
  journal={Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy},
  year={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… 

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