Non-deterministic symmetric encryption communication system based on generative adversarial networks

Symmetric encryption algorithms learned by the previous proposed end-to-end adversarial network encryption communication systems are deterministic. With the same key and same plaintext, the deterministic algorithm will lead to the same ciphertext. This means that the key in the deterministic encryption algorithm can only be used once, thus the encryption is not practical. To solve this problem, a nondeterministic symmetric encryption end-to-end communication system based on generative adversarial networks is proposed. We design a nonce-based adversarial neural network model, where a “nonce” standing for “number used only once” is passed to communication participants, and does not need to be secret. Moreover, we optimize the network structure through adding Batch Normalization (BN) to the CNNs (Convolutional Neural Networks), selecting the appropriate activation functions, and setting appropriate CNNs parameters. Results of experiments and analysis show that our system can achieve non-deterministic symmetric encryption, where Alice encrypting the same plaintext with the key twice will generate different ciphertexts, and Bob can decrypt all these different ciphertexts of the same plaintext to the correct plaintext. And our proposed system has fast convergence and the correct rate of decryption when the plaintext length is 256 or even longer.