Parallel keyed hash function construction based on chaotic neural network

Recently, various hash functions based on chaos or neural networks were proposed. Nevertheless, none of them works efficiently in parallel computing environment. In this paper, an algorithm for parallel keyed hash function construction based on chaotic neural network is proposed. The mechanism of changeable-para meter and self-synchronization establishes a close relation between the hash value bit and message, and the algorithm structure ensures the uniform sensitivity of the hash value to the message blocks at different positions. The proposed algorithm can satisfy the performance requirements of hash function. These properties make it a promising choice for hashing on parallel computing platform. (C) 2009 Elsevier B.V. All rights reserved.