High performance physical random number generator

Tsoi, Kuen Hung; Leung, Kim Hung; Leong, Philip H. W.

doi:10.1049/iet-cdt:20050173

Cited by 22 publications

(17 citation statements)

References 10 publications

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“…If the P-value for a test is determined to be equal zero, that means the sequence ap-pears to be exactly non-random and a P-value to be 1, then the sequence has perfect randomness [8].…”

Section: Nist Statistical Tests Suitementioning

confidence: 99%

Uniform Random Quantity Generator using Complete Vortex Array Technology

Deon¹,

Меняев²

2017

HoBMSTU.SIE

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One of the classic approaches in PRNGs is the middle square method in which with a simple mathematical model generating pseudorandom numbers in high speed and minimum correlation between output numbers. Despite these unique characteristics, the method contains weaknesses that a broader application of this algorithm will face. In this paper is studied middle square method and then a logistic chaotic map is introduced with its specific features and its improved weaknesses via using these characteristics. Finally the NIST tests suite s are presented, in order to detect the specific characteristics expected from truly random sequences.

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“…If the P-value for a test is determined to be equal zero, that means the sequence ap-pears to be exactly non-random and a P-value to be 1, then the sequence has perfect randomness [8].…”

Section: Nist Statistical Tests Suitementioning

confidence: 99%

Uniform Random Quantity Generator using Complete Vortex Array Technology

Deon¹,

Меняев²

2017

HoBMSTU.SIE

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“…In this work, we use a randomness extractor based on a Linear Feedback Shift Register (LFSR). The LFSRs are well known for quickly generating long pseudo-random streams with little computational resources and are in widespread use in communication applications for spectrum whitening [34][35][36][37][38] . We use a maximum length LFSR with 63 memory cells and a two-element feedback path.…”

Section: Randomness Extractionmentioning

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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“…Motivated by this need, a large number of true random number generators have been proposed in the literature, to cite a few [Bagini and Bucci 1999;Fischer and Drutarovský 2002;Tkacik 2002;Jun and Kocher 1999;Stojanovski and Kocarev 2001;Callegari et al 2005;Pareschi et al 2006;Petrie and Connelly 2000;Barak et al 2003;Liu and McNeill 2005;Tsoi et al 2007;Dichtl and Golic 2007]. The requirements for performance and quality of the generated random bitstream vary according to the application.…”

Section: Introductionmentioning

confidence: 99%

Improving the Robustness of Ring Oscillator TRNGs

Yoo¹,

Karakoyunlu

Birand

et al. 2010

ACM Trans. Reconfigurable Technol. Syst.

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A ring oscillator-based true-random number generator design (Rings design) was introduced in Sunar et al. [2007]. The design was rigorously analyzed under a simple mathematical model and its performance characteristics were established. In this article we focus on the practical aspects of the Rings design on a reconfigurable logic platform and determine their implications on the earlier analysis framework. We make recommendations for avoiding pitfalls in real-life implementations by considering ring interaction, transistor-level effects, narrow signal rejection, transmission line attenuation, and sampler bias. Furthermore, we present experimental results showing that changing operating conditions such as the power supply voltage or the operating temperature may affect the output quality when the signal is subsampled. Hence, an attacker may shift the operating point via a simple noninvasive influence and easily bias the TRNG output. Finally, we propose modifications to the design which significantly improve its robustness against attacks, alleviate implementation-related problems, and simultaneously improve its area, throughput, and power performance.

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High performance physical random number generator

Cited by 22 publications

References 10 publications

Uniform Random Quantity Generator using Complete Vortex Array Technology

Uniform Random Quantity Generator using Complete Vortex Array Technology

Random numbers from vacuum fluctuations

Improving the Robustness of Ring Oscillator TRNGs

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