Concept for a High Performance Random Number Generator Based on Physical Random Phenomena

Gude, Michael T.

doi:10.1515/freq.1985.39.7-8.187

Cited by 35 publications

(21 citation statements)

References 2 publications

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“…Then we have that a * b 1 = b 1 , and that implies a 1 , a contradiction with a * a = a. As a consequence we have that a…”

mentioning

confidence: 88%

“…There are several characteristics that separate these two classes and for our purposes this one is important. Given a finite set Q = {q 0 , q 1 The numerical experiments presented in [14] show that the percentage of linear quasigroups decreases when the order of the quasigroup increases. Furthermore, the percentage of 'bad' quasigroups, i.e.…”

Section: Overall P-value After Applying Kstest On 269 P-values = 037mentioning

confidence: 99%

“…So, it is no surprise that the proposals and implementations of RNGs range from tossing a coin, throwing a dice, drawing from a urn, drawing from a deck of cards and spinning a roulette to measuring thermal noise from a resistor and shot noise from a Zener diode or a vacuum tube, measuring radioactive decay from a radioactive source, integrating dark current from a metal insulator semiconductor capacitor, detecting locations of photoevents, and sampling a stable high-frequency oscillator with an unstable low-frequency clock. Some of the sources of randomness, such as radioactive sources [1] and quantum-mechanical sources [2], may yield data from probability distributions that are stationary. Therefore, the output of these sources does not change over time and does not depend on previous outputs.…”

Section: Introductionmentioning

confidence: 99%

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Unbiased Random Sequences from Quasigroup String Transformations

Markovski

Gligoroski

Kocarev

2005

Fast Software Encryption

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Abstract. The need of true random number generators for many purposes (ranging from applications in cryptography and stochastic simulation, to search heuristics and game playing) is increasing every day. Many sources of randomness possess the property of stationarity. However, while a biased die may be a good source of entropy, many applications require input in the form of unbiased bits, rather than biased ones. In this paper, we present a new technique for simulating fair coin flips using a biased, stationary source of randomness. Moreover, the same technique can also be used to improve some of the properties of pseudo random number generators. In particular, an improved pseudo random number generator has almost unmeasurable period, uniform distribution of the letters, pairs of letters, triples of letters, and so on, and passes many statistical tests of randomness. Our algorithm for simulating fair coin flips using a biased, stationary source of randomness (or for improving the properties of pseudo random number generators) is designed by using quasigroup string transformations and its properties are mathematically provable. It is very flexible, the input/output strings can be of 2-bits letters, 4-bits letters, bytes, 2-bytes letters, and so on. It is of linear complexity and it needs less than 1Kb memory space in its 2-bits and 4-bits implementations, hence it is suitable for embedded systems as well.

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“…Then we have that a * b 1 = b 1 , and that implies a 1 , a contradiction with a * a = a. As a consequence we have that a…”

mentioning

confidence: 88%

Section: Overall P-value After Applying Kstest On 269 P-values = 037mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unbiased Random Sequences from Quasigroup String Transformations

Markovski

Gligoroski

Kocarev

2005

Fast Software Encryption

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“…A commercial chip from AT&T generates random numbers from the same phenomenon [67]. M. Gude built a random-number generator that collected random bits from physical phenomena, such as radioactive decay [668,669]. Manfield Richter developed a randomnumber generator based on thermal noise from a semiconductor diode [1309].…”

Section: Using Random Noisementioning

confidence: 99%

Applied cryptography: Protocols, algorithms, and source code in C

1994

Computer Law & Security Review

771

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“…Early PRNG of this class were based on observing the decay statistics of radioactive nuclei 5,6 . More recently, similar PRNG based on Poisson statistics in optical photon detection were implemented [7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Random numbers from vacuum fluctuations

Shi

Chng

Kurtsiefer

2016

Applied Physics Letters

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We implement a quantum random number generator based on a balanced homodyne measurement of vacuum fluctuations of the electromagnetic field. The digitized signal is directly processed with a fast randomness extraction scheme based on a linear feedback shift register. The random bit stream is continuously read in a computer at a rate of about 480 Mbit/s and passes an extended test suite for random numbers.

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Concept for a High Performance Random Number Generator Based on Physical Random Phenomena

Cited by 35 publications

References 2 publications

Unbiased Random Sequences from Quasigroup String Transformations

Unbiased Random Sequences from Quasigroup String Transformations

Applied cryptography: Protocols, algorithms, and source code in C

Random numbers from vacuum fluctuations

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