Scalable and secure logistic regression via homomorphic encryption

Y Aono, T Hayashi, L Trieu Phong, L Wang - Proceedings of the sixth …, 2016 - dl.acm.org
Y Aono, T Hayashi, L Trieu Phong, L Wang
Proceedings of the sixth ACM conference on data and application security and …, 2016dl.acm.org
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.
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.
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