Chaos-based random number generators-part I: analysis [cryptography]

We investigate explicit functions that can produce truly random numbers. We use the analytical properties of the explicit functions to show that certain class of autonomous dynamical systems can generate random dynamics. This dynamics presents fundamental differences with the known chaotic systems. We present real physical systems that can produce this kind of random time-series. We report the results of real experiments with nonlinear circuits containing direct evidence for this new phenomenon. In particular, we show that a Josephson junction coupled to a chaotic circuit can generate unpredictable dynamics. Some applications are discussed.

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“…This system can produce unpredictable dynamics. Figure 11 shows the results of the simulation of dynamical system (32)- (36). …”

Section: Methodsmentioning

confidence: 99%

A mechanism for randomness

et al. 2002

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“…For example, RBG's based on discrete chaotic systems [4,5] present an intrinsic pseudo-random behavior superimposed to a random evolution. Therefore, even if statistical tests applied on the source output s [i] pass, that is not sufficient to state that a chaotic source produces enough entropy per bit.…”

Section: (Realised Through Systematic Exhaustion Attacks) -Even If Exmentioning

confidence: 99%

Design of Testable Random Bit Generators

Bucci

Luzzi

2005

Lecture Notes in Computer Science

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Abstract. In this paper, the evaluation of random bit generators for security applications is discussed and the concept of stateless generator is introduced. It is shown how, for the proposed class of generators, the verification of a minimum entropy limit can be performed directly on the post-processed random numbers thus not requiring a good statistic quality for the noise source itself, provided that a sufficient compression is adopted in the post-processing unit. Assuming that the noise source is stateless, a straightforward entropy estimator to drive an adaptive compression algorithm is proposed. Examples of stateless sources are also discussed.Finally, an attack scenario against a noise source is defined and an effective approach to the attack detection is presented. The entropy estimator and the attack detection together guarantee the unpredictability of the generated random numbers.

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“…Equation (1) simplifies to the binary expansion for β = 2. In the case of β encoder, β is in (1,2). For a fixed x, a 1 a 2 · · · are not unique.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, there are demands, especially for security purposes, for a physical random number generator that uses measurements of a certain physical phenomenon. Examples of physical random number generation include a random number generator using a semiconductor laser [1] or one that uses an electronic circuit based on a chaotic map [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A <I>β</I>-ary to binary conversion for random number generation using a <I>β</I> encoder

Jitsumatsu

Matsumura

2016

NOLTA

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Abstract:A β encoder is an analog-to-digital (A/D) converter, proposed by Daubechies et al. in 2002, that outputs a truncated sequence of β expansion of an input value x. It is known that the conventional pulse code modulation (PCM) that outputs the binary expansion of x is sensitive to the offset of the threshold voltage, while a β encoder is robust to such an offset. We propose an algorithm that calculates the binary expansion of an interval that is identified by an output sequence from a β encoder. Such a method is referred to as a β-ary to binary converter. We generate sequences of random numbers, using a hardware β encoder followed by the β-ary to binary converter. The randomness of the generated binary random numbers is verified by the National Institute of Standards and Technology (NIST) statistical test suite.

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Chaos-based random number generators-part I: analysis [cryptography]

Cited by 352 publications

References 29 publications

A mechanism for randomness

A mechanism for randomness

Design of Testable Random Bit Generators

A <I>β</I>-ary to binary conversion for random number generation using a <I>β</I> encoder

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Chaos-based random number generators-part I: analysis [cryptography]

Cited by 352 publications

References 29 publications

A mechanism for randomness

A mechanism for randomness

Design of Testable Random Bit Generators

A <I>&beta;</I>-ary to binary conversion for random number generation using a <I>&beta;</I> encoder

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Product

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A <I>β</I>-ary to binary conversion for random number generation using a <I>β</I> encoder