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Parallel Hash function construction based on chaotic maps with changeable parameters

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Abstract

A parallel Hash algorithm construction based on chaotic maps with changeable parameters is proposed and analyzed in this paper. The two main characteristics of the proposed algorithm are parallel processing mode and message expansion. The algorithm translates the expanded message blocks into the corresponding ASCII code values as the iteration times, iterates the chaotic asymmetric tent map and then the chaotic piecewise linear map, continuously, with changeable parameters dynamically obtained from the position index of the corresponding message blocks, to generate decimal fractions, then rounds the decimal fractions to integers, and finally cascades these integers to construct intermediate Hash value. Final Hash value with the length of 128-bit is generated by logical XOR operation of intermediate Hash values. Theoretical analysis and computer simulation indicate that the proposed algorithm satisfies the performance requirements of a secure Hash function.

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Acknowledgments

Our sincere thanks go to the anonymous reviewers for their valuable comments. The work described here was supported by the Fundamental Research Funds for the Central Universities (Grant No. CDJXS10182215), the National Natural Science Foundation of China (Grant Nos. 61070246, 61003247, 60873201), the Program for New Century Excellent Talents in University of China (NCET-09-0838, NCET-08-0603), the Natural Science Foundation Project of CQ CSTC (Grant Nos. 2010BB2047, 2009BB2211).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Yantao Li, Di Xiao, Shaojiang Deng & Qi Han

  2. Department of Computer Science, College of William and Mary, Williamsburg, VA, USA

    Yantao Li & Gang Zhou

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  1. Yantao Li
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  2. Di Xiao
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  3. Shaojiang Deng
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  4. Qi Han
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  5. Gang Zhou
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Correspondence to Yantao Li.

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Li, Y., Xiao, D., Deng, S. et al. Parallel Hash function construction based on chaotic maps with changeable parameters. Neural Comput & Applic 20, 1305–1312 (2011). https://doi.org/10.1007/s00521-011-0543-4

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  • DOI: https://doi.org/10.1007/s00521-011-0543-4

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