Secure and Smart Healthcare System using IoT and Deep Learning Models

Abstract

Patients of Smart Healthcare Systems have access to their medical records through an online portal. Due to the fact that patients do not want their names made public, maintaining data privacy and security is essential to the success of the organisation. Users are required to submit personal information to an authentication server before they can proceed with the login process. The information includes a login ID as well as a password. It is possible that the patient's adversaries will be able to violate their right to privacy if they are able to keep an eye on the patient or get in touch with them. Therefore, in this body of work, we suggest a strategy to protect the privacy of patients and the confidentiality of their medical information from dangers posed by the Authorization Service and other parties. In the course of this research, we utilised a method known as camel-based rotating panel signature. This was done not merely to protect the patients' privacy but also to protect the network itself from potential threats. The theoretical analysis of the performance of the software revealed numerous layers of security that are able to withstand a broad variety of different kinds of attacks.