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 matching, privacy enhancement without compromising security, and computation efficiency. It is essential to guarantee prediction accuracy since disease diagnosis is strongly related to health and life. In this paper, we propose efficient disease prediction that guarantees security against malicious clients and honest-but-curious server using matrix encryption technique. 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 client does not learn any information about the server's medical data. Thorough security analysis illustrates the disclosure resilience of the proposed scheme and the encryption algorithm satisfies local differential privacy. After result decryption performed by the client's device, performance is not degraded to perform prediction on encrypted data. The proposed scheme is efficient to implement real-time health monitoring.
翻译:医疗物品互联网(IOMT)的迅速发展增加了利用电子脑电图和电动心电图等各种数据类型进行实时健康监测的机会。安全问题大大妨碍了电子保健系统的实施。隐私保护系统需要解决三大挑战:准确匹配、加强隐私而不损害安全,以及计算效率。必须保证预测准确性,因为疾病诊断与健康和生命密切相关。在本文中,我们提出高效的疾病预测,保证恶意客户和诚实但可靠的服务器的安全,使用矩阵加密技术。客户提供的生物医学信号被诊断为服务器得不到关于信号的任何信息以及诊断的最终结果,而客户没有获得关于服务器医疗数据的任何信息。索罗夫安全分析显示了拟议计划的披露弹性和加密算法满足当地差异隐私。在客户设备进行断开后,对加密数据进行预测的性能不会降低。拟议的计划可以有效地实施实时健康监测。