  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.
