The rapid development in Internet of Medical Things (IoMT) boosts the opportunity for real-time health monitoring using various data types such as electroencephalography (EEG) and electrocardiography (ECG). Security issues have significantly impeded the e-healthcare system implementation. Three important challenges for privacy preserving system need to be addressed: accurate diagnosis, privacy protection without compromising accuracy, and computation efficiency. It is essential to guarantee prediction accuracy since disease diagnosis is strongly related to health and life. By implementing matrix encryption method, we propose a real-time disease diagnosis scheme using support vector machine (SVM). A biomedical signal provided by the client is diagnosed such that the server does not get any information about the signal as well as the final result of the diagnosis while the proposed scheme also achieves confidentiality of the SVM classifier and the server's medical data. The proposed scheme has no accuracy degradation. Experiments on real-world data illustrate the high efficiency of the proposed scheme. It takes less than 1 second to derive the disease diagnosis result using a device with 4Gb RAMs, suggesting the feasibility to implement real-time privacy preserving health monitoring.

翻译：医疗物联网（IoMT）的快速发展推动了利用脑电图（EEG）和心电图（ECG）等多种数据类型进行实时健康监测的机遇。然而，安全问题严重阻碍了电子医疗系统的实施。隐私保护系统需解决三个关键挑战：精确诊断、不降低精度的隐私保护以及计算效率。由于疾病诊断与健康和生命密切相关，确保预测精度至关重要。通过采用矩阵加密方法，我们提出一种基于支持向量机（SVM）的实时疾病诊断方案。该方案在客户端提供生物医学信号后，服务器无法获取有关信号及诊断最终结果的任何信息，同时还能实现SVM分类器及服务器医疗数据的机密性。所提方案没有精度损失。基于真实数据的实验证明了该方案的高效性。在配备4GB RAM的设备上，获取疾病诊断结果耗时不足1秒，这表明实现实时隐私保护健康监测具有可行性。