Applied Intelligence

, Volume 10, Issue 1, pp 71–84 | Cite as

A New Symmetric Probabilistic Encryption Scheme Based on Chaotic Attractors of Neural Networks

  • Donghui Guo
  • L.M. Cheng
  • L.L. Cheng


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.

encryption chaotic attractors neural networks symmetric-key 


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Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Donghui Guo
    • 1
    • 2
  • L.M. Cheng
    • 3
  • L.L. Cheng
    • 3
  1. 1.Department of Electronic EngineeringCity University of Hong KongHong Kong, and
  2. 2.Department of PhysicsXiamen UniversityXiamenP.R. China.
  3. 3.Department of Electronic EngineeringCity University of Hong KongHong Kong

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