Security-preserving support vector machine with fully homomorphic encryption

Abstract

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 and model privacy with the reduced communication between the servers. The proposed procedure includes our protocol, data structure and homomorphic evaluation. © 2019 CEUR-WS. All rights reserved.