  The need for medical image encryption is increasingly pronounced, for example
to safeguard the privacy of the patients' medical imaging data. In this paper,
a novel deep learning-based key generation network (DeepKeyGen) is proposed as
a stream cipher generator to generate the private key, which can then be used
for encrypting and decrypting of medical images. In DeepKeyGen, the generative
adversarial network (GAN) is adopted as the learning network to generate the
private key. Furthermore, the transformation domain (that represents the
"style" of the private key to be generated) is designed to guide the learning
network to realize the private key generation process. The goal of DeepKeyGen
is to learn the mapping relationship of how to transfer the initial image to
the private key. We evaluate DeepKeyGen using three datasets, namely: the
Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset,
and the BraTS18 dataset. The evaluation findings and security analysis show
that the proposed key generation network can achieve a high-level security in
generating the private key.
