Analysis of One-way Alterable Length Hash Function Based on Cell Neural Network

: In this paper, a hash function construction method based on cell neural network (CNN) with hyper-chaos characteristics is proposed. The chaos sequence is generated by iterating CNN with Runge-Kutta algorithm, then the sequence iterates with every bit of the plaintext continually. Then the hash code is obtained through the corresponding transform of the latter chaos sequence from iteration. Hash code with different length could be generated from the former hash result. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, and the strong stability especially, even if the hash code length is short relatively. Keyword: Cell Neural Network, One-way Hash Function, Hyper-Chaos, hash length

• Type: Conference
• Year: 2009
• Where: IEEEIAS
• Authors: Qun-ting Yang, Tie-gang Gao, Li Fan, Qiao-lun Gu
• Keywords: Cell Neural Network, Hash Code, Hash Function, IEEEIAS 2009, Information Technology