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Randomness Enhancement Using Digitalized Modified Logistic Map
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AI
DOI：10.1109/TCSII.2010.2083170.png)
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Abstract
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In this brief, a nonlinear digitalized modified logistic map-based pseudorandom number generator (DMLM-PRNG) is proposed for randomness enhancement. Two techniques, i.e., constant parameter selection and output sequence scrambling, are employed to reduce the computation cost without sacrificing the complexity of the output sequence. Statistical test results show that with only one multiplication, DMLM-PRNG passes all cases in SP800-22. Moreover, it passes most of the cases in Crush, one of the test suites of TesuU01. When compared with solutions based on digitized pseudochaotic maps previously proposed in the literature, in terms of randomness quality, our system is as good as a Renyi-map-based PRNG and better than a logistic-map-based PRNG. Moreover, compared with solutions based on a Renyi-map-based PRNG, DMLM-PRNG is better scalable to high digital resolutions with reasonable area overhead.
Keywords:
Discrete chaos
logistic map
pseudorandom number generator (PRNG)
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