An Efficient and Privacy-Preserving Outsourced Support Vector Machine Training for Internet of Medical Things
Jing Wang, Libing Wu, Huaqun Wang, Kim‐Kwang Raymond Choo, Debiao He
Abstract
As the use of machine learning in the Internet-of-Medical Things (IoMT) settings increases, so do the data privacy concerns. Therefore, in this article, we propose an efficient privacy-preserving outsourced support vector machine scheme (EPoSVM), designed for IoMT deployment. To securely train the support vector machine (SVM), we design eight secure computation protocols to allow the cloud server to efficiently execute basic integer and floating-point computations. The proposed scheme protects training data privacy and guarantees the security of the trained SVM model. The security analysis proves that our proposed protocols and EPoSVM satisfy both security and privacy protection requirements. Findings from the performance evaluation using two real-world disease data sets also demonstrate the efficiency and effectiveness of EPoSVM in achieving the same classification accuracy as a general SVM.