DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption

• URL: http://arxiv.org/abs/2012.11097v1
• Date: Mon, 21 Dec 2020 03:21:59 GMT
• Title: DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption
• Authors: Yi Ding, Fuyuan Tan, Zhen Qin, Mingsheng Cao, Kim-Kwang Raymond Choo and Zhiguang Qin
• Abstract summary: In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed. DeepKeyGen is proposed as a stream cipher generator to generate the private key. The proposed key generation network can achieve a high-level security in generating the private key.
• Score: 36.7308894683354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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