Open Access

ARTICLE

An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

by Passent El-kafrawy^1,2, Maie Aboghazalah^2,*, Abdelmoty M. Ahmed³, Hanaa Torkey⁴, Ayman El-Sayed⁴

1 School of Information Technology and Computer Science, Nile University, Giza, 12677, Egypt
2 Math and Computer Science Department, Faculty of Science, Menoufia University, Menoufia, 32511, Egypt
3 Department of Computer Engineering, College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
4 Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

* Corresponding Author: Maie Aboghazalah. Email:

(This article belongs to the Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)

Computer Modeling in Engineering & Sciences 2023, 134(2), 909-926. https://doi.org/10.32604/cmes.2022.021713

Received 10 February 2022; Accepted 11 April 2022; Issue published 31 August 2022

Abstract

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted. Two convolutional neural networks were conducted to evaluate our proposed approach: The first one is the auto-encoder, which is utilized to compress and encrypt the images, and the other assesses the classification accuracy of the image after decryption and decoding. Different hyperparameters of the encoder were tested, followed by the classification of the image to verify that no critical information was lost, to test the encryption and encoding resolution. In this approach, sixteen hyperparameter permutations are utilized, but this research discusses three main cases in detail. The first case shows that the combination of Mean Square Logarithmic Error (MSLE), ADAgrad, two layers for the auto-encoder, and ReLU had the best auto-encoder results with a Mean Absolute Error (MAE) = 0.221 after 50 epochs and 75% classification with the best result for the classification algorithm. The second case shows the reflection of auto-encoder results on the classification results which is a combination of Mean Square Error (MSE), RMSprop, three layers for the auto-encoder, and ReLU, which had the best classification accuracy of 65%, the auto-encoder gives MAE = 0.31 after 50 epochs. The third case is the worst, which is the combination of the hinge, RMSprop, three layers for the auto-encoder, and ReLU, providing accuracy of 20% and MAE = 0.485.

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Keywords

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