期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2857705.2857731
关键词
Logistic regression; additively homomorphic encryption; out sourced computation
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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