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

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    Author
    ElKafrawy, Passent cc
    Aboghazalah, Maie
    Ahmed, Abdelmoty M.
    Torkey, Hanaa
    elsayed, Ayman
    Subject
    Auto-encoder; cloud; image encryption; IoT; healthcare
    Date
    2023-01-01
    
    Metadata
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    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 details. 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 classification algorithm. The second case shows the reflection of auto-encoder results on the classification results which is a combination of Mean
    Department
    Computer Science
    Publisher
    TECH SCIENCE PRESS
    Journal title
    CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
    DOI
    10.32604/cmes.2022.021713
    ae974a485f413a2113503eed53cd6c53
    10.32604/cmes.2022.021713
    Scopus Count
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