2022/02/08 by Guanhong Miao, Miao, Guanhong, A. Adam Ding +3
Computer Science · Engineering · Neuroscience · #Brain Tumor Detection and Classification #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Other Statistics (stat.OT) #Wireless Body Area Networks
paper · pdf · doi:10.48550/arxiv.2202.03652
openalex publication_date 2022/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.