Parallel keyed hash function construction based on chaotic neural network

Authors:
Di Xiao;Xiaofeng Liao;Yong Wang
Affiliations:
College of Computer Science and Engineering, Chongqing University, Chongqing 400044, China;College of Computer Science and Engineering, Chongqing University, Chongqing 400044, China;College of Computer Science and Engineering, Chongqing University, Chongqing 400044, China and College of Economy and Management, Chongqing University of Posts and Telecommunications, Chongqing 40 ...
Venue:
Neurocomputing
Year:
2009

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Abstract

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-parameter 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.