EDG-Net: Encryption and Decryption based Gan-attention Network for CT images in the Internet of Medical Things and Telemedicine

CT images provide medical practitioners with a scientific and intuitive rationale for the diagnosis of clinical diseases. The Internet of Medical Things (IoMT) and telemedicine facilitate the preservation, transmission, and application of medical data and drive the sharing of medical data, especially medical images. Encryption and decryption of CT images distributed in the IoMT and telemedicine are becoming critical because they contain a large amount of private patient-ensitive information and are vulnerable to third-party attacks, resulting in information exposure and privacy leakage. In this paper, we propose an Encryption and Decryption based Gan-attention network (EDG-Net) for CT images in the IoMT and telemedicine. EDG-Net consists of a generator, two discriminators, a domain transfer of attention, and adaptive normalization. In addition, a double encryption and decryption strategy is introduced by EDG-Net to effectively improve the security of the ciphertext image and the fidelity of the decrypted plaintext image. Specifically, during the encryption or decryption phase, the generator transforms the CT images mutually in the plaintext and ciphertext domains. Two discriminators to identify and modify the differences between these two domain transformations, especially improve the accuracy of the reconstruction during decryption. The parameters of the trained encryption and decryption network are considered as the secret keys of encryption and decryption. Qualitative and quantitative analysis of public and private datasets demonstrates the superior performance of EDG-Net regarding encryption security and robustness as well as decryption accuracy.