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A New Symmetric Probabilistic Encryption Scheme Based on Chaotic Attractors of Neural Networks
Authors:Donghui Guo  LM Cheng  LL Cheng
Affiliation:(1) Department of Electronic Engineering, City University of Hong Kong, Hong Kong, and;(2) Department of Physics, Xiamen University, Xiamen, 361005, P.R. China. dhguo@jingxian.xmu.edu.cn;(3) Department of Electronic Engineering, City University of Hong Kong, Hong Kong
Abstract:A new probabilistic symmetric-key encryption scheme based on chaotic-classified properties of Hopfield neural networks is described. In an overstoraged Hopfield Neural Network (OHNN) the phenomenon of chaotic-attractors is well documented and messages in the attraction domain of an attractor are unpredictably related to each other. By performing permutation operations on the neural synaptic matrix, several interesting chaotic-classified properties of OHNN were found and these were exploited in developing a new cryptography technique. By keeping the permutation operation of the neural synaptic matrix as the secret key, we introduce a new probabilistic encryption scheme for a symmetric-key cryptosystem. Security and encryption efficiency of the new scheme are discussed.
Keywords:encryption  chaotic attractors  neural networks  symmetric-key
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