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

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.