202 144
Full Length Article
Fusion: Practice and Applications
Volume 8 , Issue 1, PP: 16-26 , 2022 | Cite this article as | XML | Html |PDF

Title

A New Data Fusion Model for Medical Image Encryption in IoT Environment

Authors Names :   Reem Atassi   1 *     Fuad Alhosban   2     Milan Dordevic   3  

1  Affiliation :  Higher Colleges of Technology, United Arab Emirates

    Email :  ratassi@hct.ac.ae


2  Affiliation :  Higher Colleges of Technology, United Arab Emirates

    Email :  falhosban@hct.ac.ae


3  Affiliation :  Higher Colleges of Technology, United Arab Emirates

    Email :  mdordevic@hct.ac.ae



Doi   :   https://doi.org/10.54216/FPA.080102

Received: March 12, 2022 Accepted: August 10, 2022

Abstract :

An improvement of the Internet of Things (IoT) was forecast for changing the healthcare industry and is generating the increase of the Internet of Medical Things (IoMT). The IoT revolution was surpassed the present-day human service with promise social prospects, mechanical, and financial. During this condition, it can be essential for framing an effectual approach for guaranteeing the safety and reliability of t patient’s symptomatic information which are transmitted and received in IoT criteria. This study introduces a new data fusion model in IoT environment. The proposed model is called SSOECC-MIC model focuses on the design of effective encryption scheme with optimal key generation process for IoT environment. To achieve this, the SSOECC-MIC model designs an ECC model for the encryption and decryption of medical images effectively. To further improve the security performance of the ECC model, the optimal key generation process is carried out by the use of swallow swarm optimization (SSO) algorithm. For examining the enhanced performance of the SSOECC-MIC model, a wide ranging experimental analysis is carried out. The experimental outcomes reported the betterment of the SSOECC-MIC model over recent models.

Keywords :

Security; Data Fusion; Internet of Things; Healthcare; Medical images; Encryption; Key generation

References :

[1] Avudaiappan, T., Balasubramanian, R., Pandiyan, S.S., Saravanan, M., Lakshmanaprabu, S.K. and

Shankar, K., 2018. Medical image security using dual encryption with oppositional based optimization

algorithm. Journal of medical systems, 42(11), pp.1-11.

[2] Hasan, M.K., Islam, S., Sulaiman, R., Khan, S., Hashim, A.H.A., Habib, S., Islam, M., Alyahya, S.,

Ahmed, M.M., Kamil, S. and Hassan, M.A., 2021. Lightweight encryption technique to enhance

medical image security on internet of medical things applications. IEEE Access, 9, pp.47731-47742.

[3] Akkasaligar, P.T. and Biradar, S., 2020. Selective medical image encryption using DNA

cryptography. Information Security Journal: A Global Perspective, 29(2), pp.91-101.

[4] Zhang, B., Rahmatullah, B., Wang, S.L., Zaidan, A.A., Zaidan, B.B. and Liu, P., 2020. A review of

research on medical image confidentiality related technology coherent taxonomy, motivations, open

challenges and recommendations. Multimedia Tools and Applications, pp.1-40.

[5] El-Shafai, W., Khallaf, F., El-Rabaie, E.S.M. and El-Samie, F.E.A., 2021. Robust medical image

encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare

applications. Journal of Ambient Intelligence and Humanized Computing, 12(10), pp.9007-9035.

[6] Tan, Y., Qin, J., Tan, L., Tang, H. and Xiang, X., 2018, June. A survey on the new development of

medical image security algorithms. In International Conference on Cloud Computing and Security (pp.

458-467). Springer, Cham.

[7] Balasamy, K. and Suganyadevi, S., 2021. A fuzzy based ROI selection for encryption and

watermarking in medical image using DWT and SVD. Multimedia Tools and Applications, 80(5),

pp.7167-7186.

[8] Abdulbaqi, A.S., Obaid, A.J. and Mohammed, A.H., 2021. ECG signals recruitment to implement a

new technique for medical image encryption. Journal of Discrete Mathematical Sciences and

Cryptography, 24(6), pp.1663-1673.

[9] Mishra, Z. and Acharya, B., 2020. High throughput and low area architectures of secure IoT algorithm

for medical image encryption. Journal of Information Security and Applications, 53, p.102533.

[10] Belazi, A., Talha, M., Kharbech, S. and Xiang, W., 2019. Novel medical image encryption scheme

based on chaos and DNA encoding. IEEE access, 7, pp.36667-36681.

[11] Sayah, M.M., Redouane, K. and Amine, K., 2022. A wavelet based medical image watermarking

scheme for secure transmission in telemedicine applications. Microprocessors and Microsystems,

p.104490.

[12] Lakshmi, C., Thenmozhi, K., Rayappan, J.B.B. and Amirtharajan, R., 2018. Encryption and

watermark-treated medical image against hacking disease—An immune convention in spatial and

frequency domains. Computer Methods and Programs in Biomedicine, 159, pp.11-21.

[13] Banu S, A. and Amirtharajan, R., 2020. A robust medical image encryption in dual domain: chaos-

DNA-IWT combined approach. Medical & Biological Engineering & Computing, 58(7), pp.1445-

1458.

[14] Balasamy, K. and Suganyadevi, S., 2021. A fuzzy based ROI selection for encryption and

watermarking in medical image using DWT and SVD. Multimedia Tools and Applications, 80(5),

pp.7167-7186.

[15] Hafsa, A., Sghaier, A., Malek, J. and Machhout, M., 2021. Image encryption method based on

improved ECC and modified AES algorithm. Multimedia Tools and Applications, 80(13), pp.19769-

19801.

[16] Rasina Begum, B. and Chitra, P., 2021. ECC-CRT: An Elliptical Curve Cryptographic Encryption and

Chinese Remainder Theorem based Deduplication in Cloud. Wireless Personal

Communications, 116(3), pp.1683-1702.

[17] Slezkin, A.O., Hodashinsky, I.A. and Shelupanov, A.A., 2021. Binarization of the Swallow Swarm

Optimization for Feature Selection. Programming and Computer Software, 47(5), pp.374-388.

[18] Poongodi, K. and Kumar, D., 2021. Mining serial positioning episode rules by natural exponent inertia

weight based swallow swarm optimization algorithm with constraint based event sequences. Journal of

Intelligent & Fuzzy Systems, 40(3), pp.4599-4615.

[19] Elhoseny, M., Shankar, K., Lakshmanaprabu, S.K., Maseleno, A. and Arunkumar, N., 2020. Hybrid

optimization with cryptography encryption for medical image security in Internet of Things. Neural

computing and applications, 32(15), pp.10979-10993.


Cite this Article as :
Reem Atassi , Fuad Alhosban , Milan Dordevic, A New Data Fusion Model for Medical Image Encryption in IoT Environment, Fusion: Practice and Applications, Vol. 8 , No. 1 , (2022) : 16-26 (Doi   :  https://doi.org/10.54216/FPA.080102)