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Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery
IEEE Transactions on Cloud Computing ( IF 6.5 ) Pub Date : 2020-04-01 , DOI: 10.1109/tcc.2018.2799219
Ximeng Liu , Robert H. Deng , Kim-Kwang Raymond Choo , Yang Yang

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 analytical model provider. In our approach, we design secure computation protocols to allow the cloud server to perform commonly used integer and fraction computations. To securely train the SVM, we design a secure SVM parameter selection protocol to select two SVM parameters and construct a secure sequential minimal optimization protocol to privately refresh both selected SVM parameters. The trained SVM classifier can be used to determine whether a drug chemical compound is active or not in a privacy-preserving way. Lastly, we prove that the proposed POD achieves the goal of SVM training and chemical compound classification without privacy leakage to unauthorized parties, as well as demonstrating its utility and efficiency using three real-world drug datasets.

中文翻译:

用于安全药物发现的隐私保护外包支持向量机设计

在本文中,我们提出了一个在云中保护隐私的外包药物发现框架,我们将其称为 POD。具体来说,POD 旨在允许云安全地使用多个药物配方提供者的药物配方来训练分析模型提供者提供的支持向量机 (SVM)。在我们的方法中,我们设计了安全计算协议以允许云服务器执行常用的整数和分数计算。为了安全地训练 SVM,我们设计了一个安全的 SVM 参数选择协议来选择两个 SVM 参数,并构建一个安全的顺序最小优化协议来私下刷新两个选定的 SVM 参数。经过训练的 SVM 分类器可用于以保护隐私的方式确定药物化合物是否处于活性状态。最后,
更新日期:2020-04-01
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