An overview of hybrid random number generators

The generation of random numbers is a important topic in cryptography. Random number generators are bradly divided into two categories: random number generators(RNGs) and pseudo-random number generators(PRNGs). If the PRNGs werw intensively studied in the specialized literature, many such generators being built and analyzed, the topic of RNGs did not capture the researchers atention so much. Candidates in this first category generate nondeterministic sequences and are often based on physical reactions, such as radioactive degradation or mouse movement. A special category of generators is the one that combines the two categories, namely, the category of hybrid generators (HRGs). The purpose of this paper is to study in detail the category of hybrid generators and to provide a detailed analysis of the results of statistical tests, security , portability and how to improve some of the generators of this category.

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Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

10.36227/techrxiv.17161487 ◽

2021 ◽

Author(s):

Daniel Henrique Pereira

Keyword(s):

Random Number ◽

Statistical Tests ◽

Random Numbers ◽

Microsoft Excel ◽

Chi Square ◽

Random Number Generators ◽

Pseudo Random Number ◽

Run Test

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

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Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

10.36227/techrxiv.17161487.v1 ◽

2021 ◽

Author(s):

Daniel Henrique Pereira

Keyword(s):

Random Number ◽

Statistical Tests ◽

Random Numbers ◽

Microsoft Excel ◽

Chi Square ◽

Random Number Generators ◽

Pseudo Random Number ◽

Run Test

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The Possibility of Uniform Pseudo-random Number Generation by a Group of Humans

10.31219/osf.io/38wyh ◽

2018 ◽

Author(s):

Samuel Toluwalope Ogunjo ◽

Emmanuel Jesuyon Dansu ◽

Oluwagbenga Olukanye-David ◽

Ibiyinka Agboola Fuwape

Keyword(s):

Test Score ◽

Random Number ◽

Statistical Tests ◽

Random Number Generation ◽

Random Numbers ◽

Significant Relation ◽

Random Number Generators ◽

Number Generation ◽

Pseudo Random Number ◽

University Of Technology

The ability of humans to generate numbers that are really random has always been a subject of debate. This paper investigated the possibility for a group of humans to serve as random number generators. A total of 2344 students, who were not pre-informed to avoid bias, from different faculties within the Federal University of Technology Akure were asked to chose a random number between 1 and 10. Using various statistical tests, we sought answers to the possibility of predictors like participant’s test score, gender, age and school influencing their choice of random numbers. We discovered that the numbers generated are highly random and chaotic despite number 1 being the most selected number across all predictors that was considered. Our study found that gender, test score, age did not significantly influence the choice of number while faculty showed a significant relation α < 0.05.

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Enhancing the operational efficiency of quantum random number generators

Infocommunications journal ◽

10.36244/icj.2021.2.2 ◽

2021 ◽

Vol 13 (2) ◽

pp. 10-18

Author(s):

Botond L. Márton ◽

Dóra Istenes ◽

László Bacsárdi

Keyword(s):

Random Number ◽

Statistical Tests ◽

Practical Implementation ◽

Random Numbers ◽

Random Number Generators ◽

Original Source ◽

Negative Effect ◽

Quantum Phenomena ◽

The Right

Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.

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Time-Adaptive Statistical Test for Random Number Generators

Entropy ◽

10.3390/e22060630 ◽

2020 ◽

Vol 22 (6) ◽

pp. 630 ◽

Cited By ~ 1

Author(s):

Boris Ryabko

Keyword(s):

Random Number ◽

Statistical Tests ◽

Asymptotic Properties ◽

Test Sequence ◽

P Value ◽

Specific Test ◽

Random Number Generators ◽

P Values ◽

Small Deviations ◽

The One

The problem of constructing effective statistical tests for random number generators (RNG) is considered. Currently, there are hundreds of RNG statistical tests that are often combined into so-called batteries, each containing from a dozen to more than one hundred tests. When a battery test is used, it is applied to a sequence generated by the RNG, and the calculation time is determined by the length of the sequence and the number of tests. Generally speaking, the longer is the sequence, the smaller are the deviations from randomness that can be found by a specific test. Thus, when a battery is applied, on the one hand, the “better” are the tests in the battery, the more chances there are to reject a “bad” RNG. On the other hand, the larger is the battery, the less time it can spend on each test and, therefore, the shorter is the test sequence. In turn, this reduces the ability to find small deviations from randomness. To reduce this trade-off, we propose an adaptive way to use batteries (and other sets) of tests, which requires less time but, in a certain sense, preserves the power of the original battery. We call this method time-adaptive battery of tests. The suggested method is based on the theorem which describes asymptotic properties of the so-called p-values of tests. Namely, the theorem claims that, if the RNG can be modeled by a stationary ergodic source, the value − l o g π ( x 1 x 2 … x n ) / n goes to 1 − h when n grows, where x 1 x 2 … is the sequence, π ( ) is the p-value of the most powerful test, and h is the limit Shannon entropy of the source.

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Pseudo-Random Number Generator Based on Logistic Chaotic System

Entropy ◽

10.3390/e21100960 ◽

2019 ◽

Vol 21 (10) ◽

pp. 960 ◽

Cited By ~ 12

Author(s):

Luyao Wang ◽

Hai Cheng

Keyword(s):

Chaotic System ◽

Random Number ◽

Chaotic Behavior ◽

Statistical Tests ◽

Random Sequence ◽

Random Number Generator ◽

Statistical Characteristics ◽

Random Number Generators ◽

System Parameters ◽

Pseudo Random Number

In recent years, a chaotic system is considered as an important pseudo-random source to pseudo-random number generators (PRNGs). This paper proposes a PRNG based on a modified logistic chaotic system. This chaotic system with fixed system parameters is convergent and its chaotic behavior is analyzed and proved. In order to improve the complexity and randomness of modified PRNGs, the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack. It is hard to speculate on the pseudo-random number by chaotic behavior because there is no statistical characteristics and infer the pseudo-random number generated by chaotic behavior. The system parameters of the next chaotic system are related to the chaotic values generated by the previous ones, which makes the PRNG generate enough results. By confusing and rearranging the output sequence, the system parameters of the previous time cannot be gotten from the next time which ensures the security. The analysis shows that the pseudo-random sequence generated by this method has perfect randomness, cryptographic properties and can pass the statistical tests.

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Pseudorandom Number Generators

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Formation Methods, Models, and Hardware Implementation of Pseudorandom Number Generators ◽

10.4018/978-1-5225-2773-2.ch001 ◽

2017 ◽

pp. 1-25

Keyword(s):

Random Number ◽

Pseudorandom Number ◽

Random Numbers ◽

Pseudorandom Sequences ◽

Random Number Generators ◽

Random Sequences ◽

Pseudorandom Number Generators ◽

Pseudo Random Number

In this chapter, the author considers existing methods and means of forming pseudo-random sequences of numbers and also are described the main characteristics of random and pseudorandom sequences of numbers. The main theoretical aspects of the construction of pseudo-random number generators are considered. Classification of pseudorandom number generators is presented. The structures and models of the most popular pseudo-random number generators are considered, the main characteristics of generators that affect the quality of the formation of pseudorandom bit sequences are described. The models of the basic mathematical generators of pseudo-random numbers are considered, and also the principles of building hardware generators are presented.

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Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

International Symposium on Mathematics, Quantum Theory, and Cryptography - Mathematics for Industry ◽

10.1007/978-981-15-5191-8_6 ◽

2020 ◽

pp. 17-37

Author(s):

Kentaro Tamura ◽

Yutaka Shikano

Keyword(s):

Random Number ◽

Quantum Computer ◽

Statistical Tests ◽

Random Number Generator ◽

Random Number Generation ◽

Black Box ◽

Random Numbers ◽

Number Generator ◽

Random Number Generators ◽

Simple Indicator

Abstract A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.

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A New Statistical Test for PRNG Based on the Attendance’s Law

Journal of Advances in Mathematics and Computer Science ◽

10.9734/jamcs/2021/v36i130328 ◽

2021 ◽

pp. 37-46

Author(s):

Babacar Alasane Ndaw ◽

Ousmane Ndiaye ◽

Mamadou Sanghar´e ◽

Cheikh Thi´ecoumba Gueye

Keyword(s):

Random Number ◽

Statistical Tests ◽

Random Number Generator ◽

Deterministic Algorithm ◽

Statistical Test ◽

The Other ◽

Cryptographic Primitives ◽

Random Number Generators ◽

Other Hand ◽

The One

One family of the cryptographic primitives is random Number Generators (RNG) which have several applications in cryptography such that password generation, nonce generation, Initialisation vector for Stream Cipher, keystream. Recently they are also used to randomise encryption and signature schemes. A pseudo-random number generator (PRNG) or a pseudo-random bit generator (PRBG) is a deterministic algorithm that produces numbers whose distribution is on the one hand indistinguishable from uniform ie. that the probabilities of appearance of the different symbols are equal and that these appearances are all independent. On the other hand, the next output of a PRNG must be unpredictable from all its previous outputs. Indeed, A set of statistical tests for randomness has been proposed in the literature and by NIST to evaluate the security of random(pseudo) bit or block. Unfortunately there are non-random binary streams that pass these standardized tests. In this pap er, as outcome, we intro duce on the one hand a new statistical test in a static contextcalled attendance’s law and on the other hand a distinguisher based on this new attendance’s law.

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Novel Memristor Based True Random Number Generator

10.18122/td/1770/boisestate ◽

2020 ◽

Author(s):

Scott Stoller

Keyword(s):

Random Number ◽

Statistical Tests ◽

Random Number Generator ◽

Circuit Board ◽

Random Numbers ◽

Number Generator ◽

Random Number Generators ◽

Coin Toss ◽

True Random Number Generator ◽

Digital World

Random numbers are an important, but often overlooked part of the modern computing environment. They are used everywhere around us for a variety of purposes, from simple decision making in video games such as a coin toss, to securing financial transactions and encrypting confidential communications. They are even useful for gambling and the lottery. Random numbers are generated in many ways. Pseudo random number generators (PRNGs) generate numbers based on a formula. True random number generators (TRNGs) capture entropy from the environment to generate randomness. As our society and our devices become more connected in the digital world, it is important to develop new ways to generate truly random numbers in order to secure communications and connected devices. In this work a novel memristor-based True Random Number Generator is designed and a physical implementation is fabricated and tested using a W-based self-directed channel (SDC) memristor. The circuit was initially designed and prototyped on a breadboard. A custom Printed Circuit Board (PCB) was fabricated for the final circuit design and testing of the novel memristor-based TRNG. The National Institute of Standards and Technology (NIST) Statistical Test Suite (STS) was used to check the output of the TRNG for randomness. The TRNG was demonstrated to pass 13 statistical tests out of the 15 in the STS.

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