• Title/Summary/Keyword: SEI(Source of Entropy Input)

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An Approach to Constructing an Efficient Entropy Source on Multicore Processor (멀티코어 환경에서 효율적인 엔트로피 원의 설계 기법)

  • Kim, SeongGyeom;Lee, SeungJoon;Kang, HyungChul;Hong, Deukjo;Sung, Jaechul;Hong, Seokhie
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.28 no.1
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    • pp.61-71
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    • 2018
  • In the Internet of Things, in which plenty of devices have connection to each other, cryptographically secure Random Number Generators (RNGs) are essential. Particularly, entropy source, which is the only one non-deterministic part in generating random numbers, has to equip with an unpredictable noise source(or more) for the required security strength. This might cause an requirement of additional hardware extracting noise source. Although additional hardware resources has better performance, it is needed to make the best use of existing resources in order to avoid extra costs, such as area, power consumption. In this paper, we suggest an entropy source which uses a multi-threaded program without any additional hardware. As a result, it reduces the difficulty when implementing on lightweight, low-power devices. Additionally, according to NIST's entropy estimation test suite, the suggested entropy source is tested to be secure enough for source of entropy input.

The Entropy of Recursively-Indexed Geometric Distribution

  • Sangsin Na;Kim, Young-Kil;Lee, Haing-Sei
    • Journal of Electrical Engineering and information Science
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    • v.1 no.1
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    • pp.91-97
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    • 1996
  • This paper proves by straightforward computation an interesting property of a recursive indexing: it preserves the entropy of a geometrically-distributes stationary memoryless source. This result is a pleasant surprise because the recursive indexing though one-to-one, is a symbol-to-string mapping and the entropy is measured in terms of the source symbols. This preservation of the entropy implies that the minimum average number of bits needed to represent a geometric memoryless source by the recursive indexing followed by a good binary encoder of a finite imput alphabet remains the same as that by a good encoder of an infinite input alphabet. Therefore, the recursive indexing theoretically keeps coding optimality intact. For this reason recursive indexing can provide an interface for a binary code with a finite code book that performs reasonably well for a source with an infinite alphabet.

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