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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

Mesfer Al Duhayyim1,*, Mohammed Maray2, Ayman Qahmash2, Fatma S. Alrayes3, Nuha Alshuqayran4, Jaber S. Alzahrani5, Mohammed Alghamdi2,6, Abdullah Mohamed7

1 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Saudi Arabia
2 Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4 Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia
5 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
6 Department of Information and Technology Systems, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
7 Research Centre, Future University in Egypt, New Cairo, 11845, Egypt

* Corresponding Author: Mesfer Al Duhayyim. Email: email

Computers, Materials & Continua 2023, 74(2), 3133-3149. https://doi.org/10.32604/cmc.2023.032740

Abstract

Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services’ domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data. The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security (IMLOSIE-MIS) technique for IoT environment. The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively. To do so, the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image. Besides, shadow image encryption process takes place with the help of Multileader Optimization (MLO) with Homomorphic Encryption (IMLO-HE) technique, where the optimal keys are generated with the help of MLO algorithm. On the receiver side, decryption process is initially carried out and shadow image reconstruction process is conducted. The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models. The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

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Cite This Article

APA Style
Duhayyim, M.A., Maray, M., Qahmash, A., Alrayes, F.S., Alshuqayran, N. et al. (2023). Improved multileader optimization with shadow encryption for medical images in iot environment. Computers, Materials & Continua, 74(2), 3133-3149. https://doi.org/10.32604/cmc.2023.032740
Vancouver Style
Duhayyim MA, Maray M, Qahmash A, Alrayes FS, Alshuqayran N, Alzahrani JS, et al. Improved multileader optimization with shadow encryption for medical images in iot environment. Comput Mater Contin. 2023;74(2):3133-3149 https://doi.org/10.32604/cmc.2023.032740
IEEE Style
M.A. Duhayyim et al., "Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment," Comput. Mater. Contin., vol. 74, no. 2, pp. 3133-3149. 2023. https://doi.org/10.32604/cmc.2023.032740



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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