An Efficient Encryption and Compression of Sensed IoTMedical Images Using Auto-Encoder.
SubjectCONVOLUTIONAL neural networks; IMAGE encryption; CLASSIFICATION algorithms; CHANNEL coding
Auto-encoder cloud healthcare image encryption IoT
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AbstractHealthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption ofmedical images is very important to secure patient information. Encrypting these images consumes a lot of time onedge computing; therefore, theuse of anauto-encoder for compressionbefore encodingwill solve such a problem. In this paper, we use an auto-encoder to compress amedical 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 ofMean 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.
Journal titleCMES-Computer Modeling in Engineering & Sciences