Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

@article{Aono2016PrivacyPreservingLR,
  title={Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption},
  author={Yoshinori Aono and Takuya Hayashi and Le Trieu Phong and Lihua Wang},
  journal={IEICE Transactions},
  year={2016},
  volume={99-D},
  pages={2079-2089}
}
Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we… CONTINUE READING

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