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Chin. Phys. B, 2008, Vol. 17(7): 2388-2393    DOI: 10.1088/1674-1056/17/7/011
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One-way hash function based on hyper-chaotic cellular neural network

Yang Qun-Ting(杨群亭) and Gao Tie-Gang(高铁杠)
College of Software, Nankai University, Tianjin 300071, China
Abstract  The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge--Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.
Keywords:  one-way hash function      hyper-chaos      cellular neural network      Runge--Kutta formula  
Received:  22 November 2007      Revised:  31 December 2007      Accepted manuscript online: 
PACS:  03.67.Dd (Quantum cryptography and communication security)  
  05.45.Pq (Numerical simulations of chaotic systems)  
  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
Fund: Project supported by Key Program of Natural Science Fund of Tianjin of China (Grant No 07JCZDJC06600).

Cite this article: 

Yang Qun-Ting(杨群亭) and Gao Tie-Gang(高铁杠) One-way hash function based on hyper-chaotic cellular neural network 2008 Chin. Phys. B 17 2388

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