Key Resilient Encryption Algorithm Based on Generative Adversarial Networks

LI Ximing, WU Jiarun, WU Shaoqian, GUO Yubin, MA Sha

1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
• Online:2020-05-15 Published:2020-05-13

基于生成对抗网络的抗泄露加密算法研究

1. 华南农业大学 数学与信息学院，广州 510642

Abstract:

Generative Adversarial Networks（GANs） is a kind of deep learning model. Through adversarial training with discriminative model, a gradually improved generative model can be obtained to generate data which are difficult to distinguish between true and false. And using GANs to realize encryption algorithm is a new research direction. Firstly, under the 16 bit key symmetric encryption scheme, this paper tests the key resilient encryption communication of the basic encryption communication model built by Abadi et al., and finds the possibility of realizing the key resilient encryption communication by using GANs. Then, the neural network model of both parties and the adversary is improved by modifying the activation function of the network, and obtains the encryption algorithm model in the case of 3 bit key leakage. The communication stability can be improved by increasing the complexity of the decrypter and the adversary model. Then, adding batch normalization in the model can further improve the ability of key resilient encryption communication. Finally, in the case of 8 bit leakage, it can ensure the normal communication between the two sides of the communication and the adversary cannot obtain the secret information. This paper provides a new solution to the problem of key resilient encryption communication and proves the feasibility of the solution through experiments.