一种新的基于神经网络混沌吸引子的公钥密码算法

论述了一种新的基于神经网络混沌吸引子的公钥密码算法，在过饱和贮存的Hopfield神经网络模型中混沌吸引子与初始状态之间存在一种单向函数关系，改变神经网络的联结权矩阵，混沌吸引子及其相应的吸引域会随之发生改变，如果以联结权矩阵为陷门，并利用可交换的随机变换矩阵来改变神经网络的联结权矩阵，则可以创建一种新的Diffie-Hellman公钥加密算法．将随机变换矩阵作为私钥，而将变换后的神经网络联结突触矩阵作为公钥，介绍了这种新的公钥加密方案，并分析和讨论其安全性和加密效率。

A New Public-key Cryptography Based on Chaotic Attractors of Neural Networks

A new public-key cryptography based on chaotic attractors of neural networks is described. There is a one-way function between chaotic attractors and initial states in an Overstoraged Hopfield Neural Networks (OHNN), and each attractor and its correspond ing domain of attraction are changed with permuta- tion operations on the neural synaptic matrix. If the neural synaptic matrix is used as a trap door and changed by commutative random permutation matrix, a new cryptography technique accord ing to D iffie-Hellman public- key cryptosystem is proposed. By keeping the random permutation operation of the neural synaptic matrix as the secret key, and the neural synaptic matrix after permutation as public-key, a new encryption scheme for a public-key cryptosystem is introduced. Security of the new scheme is d iscussed.