Enhancing healthcare data security using RFE and CRHSM for big data

Providing security to the medical big data stored in healthcare cloud systems is the most exciting and demanding task in the present day. Many researchers use cryptographic techniques to protect big data against malicious users/attacks in cloud environments. Still, they face the problems of high complexity in operations, increased time consumption, storage overhead, and lack of efficiency. Hence, this paper aims to develop a new extensive data security framework for improving the reliability of healthcare systems. The main contribution of this work is to introduce a novel Cohesive Random-Hash based Security Model (CRHSM) for securing both user authentication and medical records. Here, the hospital environment is considered with the entities of patients, healthcare professionals, and hospital servers. In this environment, the registered entities can only participate in communication, including system initialization, registration, login, authentication, encryption, and decryption modules. First, the entities must register with the hospital server to obtain the smart card for further communications. Here, the Squirrel Search Optimization (SSO) technique generates the random number used for the registration phase. During the login and authentication module, the legitimacy of patients and healthcare professionals is validated based on the authentication parameters. Moreover, the medical records are encrypted before storing them in the cloud systems using an efficient Reformist Feistel Encryption (RFE) mechanism. Moreover, various evaluation parameters are considered for assessing the performance of the proposed model, and the evaluated values are compared with security approaches to validate the effectiveness of the proposed scheme.