A Symmetric Probabilistic Encryption Scheme Based On CHNN Without Data Expansion

Authors:
K. C. Leung;S. L. Li;L. M. Cheng;C. K. Chan
Affiliations:
Department of Computer Engineering, Information Technology, City University of Hong Kong, Hong Kong, China;Department of Computer Engineering, Information Technology, City University of Hong Kong, Hong Kong, China;Department of Computer Engineering, Information Technology, City University of Hong Kong, Hong Kong, China;Department of Computer Engineering, Information Technology, City University of Hong Kong, Hong Kong, China
Venue:
Neural Processing Letters
Year:
2006

Citing 4
Cited 0

A method for obtaining digital signatures and public-key cryptosystems

Communications of the ACM
Shift Register Sequences

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

Applied Intelligence
The convergence properties of a clipped Hopfield network and its application in the design of keystream generator

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In 1999, Guo et al. proposed a new probabilistic symmetric probabilistic encryption scheme based on chaotic attractors of neural networks. The scheme is based on chaotic properties of the Overstoraged Hopfield Neural Network (OHNN). The approach bridges the relationship between neural network and cryptography. However, there are some problems in their scheme: (1) exhaustive search is needed to find all the attractors; (2) the data expansion in the paper is wrongly derived; (3) problem exists on creating the synaptic weight matrix. In this letter, we propose a symmetric probabilistic encryption scheme based on Clipped Hopfield Neural Network (CHNN), which solves the above mentioned problems. Furthermore, it keeps the length of the ciphertext equals to that of the plaintext.