School of Information Engineering, Xuchang University, Xuchang, Henan, China.
No.1 Middle School of Zhengzhou, Zhengzhou, Henan, China.
PLoS One. 2019 May 29;14(5):e0217349. doi: 10.1371/journal.pone.0217349. eCollection 2019.
With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients. However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient. Each party learns nothing about the other's private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure.
随着机器学习和云计算的繁荣，大量电子医疗数据中可以挖掘出有意义的信息，帮助医生为患者做出适当的疾病诊断。然而，频繁使用患者的医疗数据和疾病信息会引起隐私问题。在本文中，我们基于单层感知器，提出了一种基于云支持的隐私保护临床决策方案（PPCD），该方案可安全地为患者进行疾病模型训练和预测。各方都无法了解对方的隐私信息。在 PPCD 中，提出并引入了一种轻量级安全乘法，以提高模型训练的效率。对真实数据的安全性分析和实验结果表明，所提出的 PPCD 在不泄露隐私风险的情况下，能够实现疾病预测的高精度。
