Privacy-preserving outsourced support vector machine design for secure drug discovery

• Main Authors: LIU, Ximeng, DENG, Robert H., CHOO, Kim-Kwang Raymond, YANG, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Cloud-supported drug discovery; privacy-preserving; support vector machine; sequential minimal optimization; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5309; https://ink.library.smu.edu.sg/context/sis_research/article/6312/viewcontent/Privacy_preserving_outsourced_support_av.pdf
• Institution: Singapore Management University

In this paper, we propose a framework for privacy-preserving outsourced drug discovery in the cloud, which we refer to as POD. Specifically, POD is designed to allow the cloud to securely use multiple drug formula providers' drug formulas to train Support Vector Machine (SVM) provided by the an...