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

摘要：

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 (CNN) with hyper-chaos characteristics is proposed. The chaos sequence generated by iterating CNN with Runge-Kutta algorithm, then the sequence iterates with every bit of the plaintext continually. Then 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, especially the strong stability, even if the hash code length is short relatively.

关键词： cell neural network one-way hash function Hyper-chaos hash length

作者: Qun-ting Yang Tie-gang Gao Li Fan Qiao-lun Gu

作者单位: College of Software,Nankai University,300071,Tianjin,China Information and Technology College,Tianjin University of Technology and Education 300222,Tianjin,Chi

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 391-395

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）