@Article{cmes.2022.021713,
AUTHOR = {Passent El-kafrawy, Maie Aboghazalah, Abdelmoty M. Ahmed, Hanaa Torkey, Ayman El-Sayed},
TITLE = {An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {2},
PAGES = {909--926},
URL = {http://www.techscience.com/CMES/v134n2/49511},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2022.021713}
}