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.

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Author Of this post: <a href="">Guanhong Miao</a>, <a href="">A. Adam Ding</a>, <a href="">Samuel S. Wu</a>

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