Analysis of Min-Entropy Estimation of Markov Chain Output Sequences 


Vol. 43,  No. 12, pp. 1987-1997, Dec.  2018
10.7840/kics.2018.43.12.1987


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  Abstract

Markov chain is a stochastic model for the phenomena where information about the present and past is given, information about the future is determined only by the current information without relying on past information. Cryptographically secure random number generators that generate important elements such as encryption keys in cryptographic systems should use unpredictable noise sources with sufficient entropy. Among the various min-entropy estimation methods for noise sources, an estimation method based on the Markov chain model is designed to detect a dependency that may exist between output random numbers. In this paper, we prove that the minimum entropy of the output bitstream under the first Markov chain model can be estimated by the bitstream that we have classified into three types. Based on the proposed theory, we experimentally confirm that Markov estimator of SP 800-90B, which is a NIST standard for evaluating the min-entropy of a random number generator, is suitable with the theoretical min-entropy.

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  Cite this article

[IEEE Style]

W. Kim, H. Park, Y. Yeom, J. Kang, "Analysis of Min-Entropy Estimation of Markov Chain Output Sequences," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 12, pp. 1987-1997, 2018. DOI: 10.7840/kics.2018.43.12.1987.

[ACM Style]

Wontae Kim, Hojoong Park, Yongjin Yeom, and Ju-Sung Kang. 2018. Analysis of Min-Entropy Estimation of Markov Chain Output Sequences. The Journal of Korean Institute of Communications and Information Sciences, 43, 12, (2018), 1987-1997. DOI: 10.7840/kics.2018.43.12.1987.

[KICS Style]

Wontae Kim, Hojoong Park, Yongjin Yeom, Ju-Sung Kang, "Analysis of Min-Entropy Estimation of Markov Chain Output Sequences," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 12, pp. 1987-1997, 12. 2018. (https://doi.org/10.7840/kics.2018.43.12.1987)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880