This paper deals with one of the key problems of e-healthcare which is the security. Patients are worried about the confidentiality of their electronic medical record (EMR) which could be used to expose their identities. It is high time to revisit the confidentiality and security issues of the existing telehealth system. Intruders can perform sniffing, spoofing, or phishing operations effortlessly during the online exchange of the EMR using a digital platform. The EMR must be transmitted anonymously with a high degree of hardness of encryption by protecting the authentication, confidentiality, and integrity criteria of the patient. These requirements recommend the security of the current system to be improved. In this paper, a neural synchronization-guided concatenation of header and secret shares with the ability to transmit the EMR with an end-to-end security protocol has been proposed. This proposed methodology breaks down the EMR into the n number of secret shares and transmits to the n number of recipients. The original EMR can be reconstructed after the amalgamation of a minimum k (threshold) number of secret shares. The novelty of the technique is that one share should come from a specific recipient to whom a special privilege is given to recreate the EMR among such a predefined number of shares. In the absence of this privileged share, the original EMR cannot be reconstructed. This proposed technique has passed various parametric tests. The results are compared with existing benchmark techniques. The results of the proposed technique have shown robust and effective potential.
Keywords: Artificial neural networks (ANNs); COVID-19; Electronic medical record (EMR); Secret share; Security; Telehealth.
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