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Severity Based Light-Weight Encryption Model for Secure Medical Information System

Firas Abedi1, Subhi R.M. Zeebaree2, Zainab Salih Ageed3, Hayder M.A. Ghanimi4, Ahmed Alkhayyat5,*, Mohammed A.M. Sadeeq6, Sarmad Nozad Mahmood7, Ali S. Abosinnee8, Zahraa H. Kareem9, Ali Hashim Abbas10, Waleed Khaild Al-Azzawi11, Mustafa Musa Jaber12,13, Mohammed Dauwed14

1 Department of Mathematics, College of Education, Al-Zahraa University for Women, Karbala, Iraq
2 Energy Eng. Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq
3 Computer Science Department, College of Science, Nawroz University, Duhok, Iraq
4 Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
5 College of Technical Engineering, The Islamic University, Najaf, Iraq
6 ITM Department, Technical College of Administration, Duhok Polytechnic University, Duhok, Iraq
7 Computer Technology Engineering, College of Engineering Technology, Al-Kitab University, Iraq
8 Altoosi University College, Najaf, Iraq
9 Department of Medical Instrumentation Techniques Engineering, Al-Mustaqbal University College, Hillah, 51001, Iraq
10 College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna, 66002, Iraq
11 Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Bagdad, Iraq
12 Department of Medical Instruments Engineering Techniques, Al-Turath University College, Baghdad, 10021, Iraq
13 Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10021, Iraq
14 Department of Medical Instrumentations Techniques Engineering, Dijlah University College, Baghdad, Iraq

* Corresponding Author: Ahmed Alkhayyat. Email: email

Computers, Materials & Continua 2023, 74(3), 5691-5704.


As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren’t needed can be saved by using this method, which cuts down on security costs. Encryption is one of the most effective methods for protecting medical images after this step. Confusion and dispersion are two fundamental encryption processes. A new encryption algorithm for very sensitive data is developed in this study. Picture splitting with image blocks is now developed by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks. After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption. For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB). Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map. Once all of the blocks have been encrypted, they are combined to create encrypted images. The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.


Cite This Article

APA Style
Abedi, F., Zeebaree, S.R., Ageed, Z.S., Ghanimi, H.M., Alkhayyat, A. et al. (2023). Severity based light-weight encryption model for secure medical information system. Computers, Materials & Continua, 74(3), 5691-5704.
Vancouver Style
Abedi F, Zeebaree SR, Ageed ZS, Ghanimi HM, Alkhayyat A, Sadeeq MA, et al. Severity based light-weight encryption model for secure medical information system. Comput Mater Contin. 2023;74(3):5691-5704
IEEE Style
F. Abedi et al., "Severity Based Light-Weight Encryption Model for Secure Medical Information System," Comput. Mater. Contin., vol. 74, no. 3, pp. 5691-5704. 2023.

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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