Open Access

ARTICLE

Encryption with User Authentication Model for Internet of Medical Things Environment

K. S. Riya¹, R. Surendran^2,*, Carlos Andrés Tavera Romero³, M. Sadish Sendil⁴

1 Department of Information Technology, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
2 Center for Artificial Intelligence and Research (CAIR), Chennai Institute of Technology, Chennai, 600069, India
3 COMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de Cali, Cali, 76001, Colombia
4 Department of Emerging Technologies, Guru Nanak Institute of Technology, Ibrahipatnam, 501506, Telangana, India

* Corresponding Author: R. Surendran. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 507-520. https://doi.org/10.32604/iasc.2023.027779

Received 25 January 2022; Accepted 28 February 2022; Issue published 06 June 2022

Abstract

Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

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Keywords

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