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Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

Mohammad Alamgeer1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Nazik Alturki3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Ishfaq Yaseen4, Mohamed I. Eldesouki5

1 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
2 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
5 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2022, 73(1), 1159-1175.


Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm (DFA) with signcryption technique to encrypt the medical images captured by the IoT devices. Besides, blockchain (BC) can be utilized as a distributed data saving approach for generating a ledger, which permits access to the users and prevents third party’s access to encrypted data. In addition, the classification process includes SqueezeNet based feature extraction, softmax classifier (SMC), and Nadam based hyperparameter optimizer. The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture. For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique, a comprehensive experimental analysis is carried out. The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.


Cite This Article

M. Alamgeer, S. S. Alotaibi, S. Al-Otaibi, N. Alturki, A. Mustafa Hilal et al., "Privacy preserving image encryption with deep learning based iot healthcare applications," Computers, Materials & Continua, vol. 73, no.1, pp. 1159–1175, 2022.

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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