A Secure COVID Affected CT Scan Image Encryption Scheme Using Hybrid MLSCM for IoMT Environment
Sasmita Padhy, Sachikanta Dash, Naween Kumar, Gyanendra Kumar
Abstract
The 2019 coronavirus disease (COVID-19) spread quickly throughout the world, causing a global pandemic. After proper diagnosis, digital images storing patient personal health information are saved and transferred over a public network. As a result, the security aspects of preserving sensitive patient health information are becoming crucial. In this study, we presented an encryption system for COVID-19 CT images to address this problem. This work describes an effective inter-intra bit level (BL) pixel processing solution for secret medical images through an encryption technique that does not rely on pixel correlation. The hybrid multi-logistic sine chaotic map (MLSCM) technique combines logistic and sine maps, improving randomness and ergodicity and leading to stronger security. The utilization of hybrid MLSCM technology further enhances the time efficiency. The system’s security is strengthened by the simultaneous rearrangement and spreading of pixels, which helps to prevent statistical and frequent types of attacks. The SHA-512 hash algorithm is incorporated to generate highly secure keys, ensuring resilience against brute force and differential attacks. Row-wise and column-based permutation algorithms further enhance security by disrupting statistical processes. The suggested method yields an optimal entropy of 7.9982, which is more significant than the comparable recorded works. The proposed work beats other existing work on calculating the number of pixel change rates (NPCR) and the unified average change intensity (UACI), with values of 99.6233 and 33.5065, respectively. The avalanche impact for key sensitive analysis has been confirmed with a key space of 1.5491 × 2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">526</sup>, enough to resist multiple attacks.