DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things
This work addresses privacy protection for medical images in IoMT systems, but it is incremental as it adapts existing Cycle-GAN methods to a specific domain.
The authors tackled the challenge of securing sensitive medical images in the Internet of Medical Things by proposing DeepEDN, a deep learning-based encryption and decryption network, which achieved a high level of security with good efficiency on a chest X-ray dataset.
Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the Cycle-Generative Adversarial Network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest(ROI)-mining-network is proposed to extract the interested object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray dataset. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.