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Security-preserving Support Vector Machine with Fully Homomorphic Encryption
2019
AAAI Conference on Artificial Intelligence
Recently, security issues have become more and more important to apply machine learning models to a real-world problem. It is necessary to preserve the data privacy for using sensitive data and to protect the information of a trained model for defending the intentional attacks. In this paper, we want to propose a security-preserving learning framework using fully homomorphic encryption for support vector machine model. Our approach aims to train the model on encrypted domain to preserve data
dblp:conf/aaai/Park0CLKB19
fatcat:qdk4pwus25gmhmxebygcb3cagy