  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.
