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Random number generation

About: Random number generation is a research topic. Over the lifetime, 4404 publications have been published within this topic receiving 82421 citations. The topic is also known as: RNG.


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ReportDOI
20 Dec 2000
TL;DR: Some criteria for characterizing and selecting appropriate generators and some recommended statistical tests are provided, as a first step in determining whether or not a generator is suitable for a particular cryptographic application.
Abstract: : This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may he used in many cryptographic applications, such as the generation of key material. Generators suitable for use in cryptographic applications may need to meet stronger requirements than for other applications. In particular, their outputs must he unpredictable in the absence of knowledge of the inputs. Some criteria for characterizing and selecting appropriate generators are discussed in this document. The subject of statistical testing and its relation to cryptanalysis is also discussed, and some recommended statistical tests are provided. These tests may he useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. The design and cryptanalysis of generators is outside the scope of this paper.

3,059 citations

Book ChapterDOI
04 Oct 2019
TL;DR: A constructive theory of randomness for functions, based on computational complexity, is developed, and a pseudorandom function generator is presented that has applications in cryptography, random constructions, and complexity theory.
Abstract: A constructive theory of randomness for functions, based on computational complexity, is developed, and a pseudorandom function generator is presented. This generator is a deterministic polynomial-time algorithm that transforms pairs (g, r), where g is any one-way function and r is a random k-bit string, to polynomial-time computable functionsf,: { 1, . . . , 2') + { 1, . . . , 2kl. Thesef,'s cannot be distinguished from random functions by any probabilistic polynomial-time algorithm that asks and receives the value of a function at arguments of its choice. The result has applications in cryptography, random constructions, and complexity theory. Categories and Subject Descriptors: F.0 (Theory of Computation): General; F. 1.1 (Computation by Abstract Devices): Models of Computation-computability theory; G.0 (Mathematics of Computing): General; G.3 (Mathematics of Computing): Probability and Statistics-probabilistic algorithms; random number generation

1,679 citations

Journal ArticleDOI
15 Apr 2010-Nature
TL;DR: It is shown that the non-local correlations of entangled quantum particles can be used to certify the presence of genuine randomness, and it is thereby possible to design a cryptographically secure random number generator that does not require any assumption about the internal working of the device.
Abstract: True randomness does not exist in classical physics, where randomness is necessarily a result of forces that may be unknown but exist. The quantum world, however, is intrinsically truly random. This is difficult to prove, as it is not readily distinguishable from noise and other uncontrollable factors. Now Pironio et al. present proof of a quantitative relationship between two fundamental concepts of quantum mechanics — randomness and the non-locality of entangled particles. They first show theoretically that the violation of a Bell inequality certifies the generation of new randomness, independently of any implementation details. To illustrate the approach, they then perform an experiment in which — as confirmed using the theoretical tools that they developed — 42 new random bits have been generated. As well as having conceptual implications, this work has practical implications for cryptography and for numerical simulation of physical and biological systems. Here it is shown, both theoretically and experimentally, that non-local correlations between entangled quantum particles can be used for a new cryptographic application — the generation of certified private random numbers — that is impossible to achieve classically. The results have implications for future device-independent quantum information experiments and for addressing fundamental issues regarding the randomness of quantum theory. Randomness is a fundamental feature of nature and a valuable resource for applications ranging from cryptography and gambling to numerical simulation of physical and biological systems. Random numbers, however, are difficult to characterize mathematically1, and their generation must rely on an unpredictable physical process2,3,4,5,6. Inaccuracies in the theoretical modelling of such processes or failures of the devices, possibly due to adversarial attacks, limit the reliability of random number generators in ways that are difficult to control and detect. Here, inspired by earlier work on non-locality-based7,8,9 and device-independent10,11,12,13,14 quantum information processing, we show that the non-local correlations of entangled quantum particles can be used to certify the presence of genuine randomness. It is thereby possible to design a cryptographically secure random number generator that does not require any assumption about the internal working of the device. Such a strong form of randomness generation is impossible classically and possible in quantum systems only if certified by a Bell inequality violation15. We carry out a proof-of-concept demonstration of this proposal in a system of two entangled atoms separated by approximately one metre. The observed Bell inequality violation, featuring near perfect detection efficiency, guarantees that 42 new random numbers are generated with 99 per cent confidence. Our results lay the groundwork for future device-independent quantum information experiments and for addressing fundamental issues raised by the intrinsic randomness of quantum theory.

1,337 citations

Journal ArticleDOI
TL;DR: In this paper, practical and theoretical issues concerning the design, implementation, and use of a good, minimal standard random number generator that will port to virtually all systems are presented concerning the use of such a generator.
Abstract: Practical and theoretical issues are presented concerning the design, implementation, and use of a good, minimal standard random number generator that will port to virtually all systems.

1,260 citations

Journal ArticleDOI
TL;DR: TestU01 as discussed by the authors is a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs).
Abstract: We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several others tests proposed in the literature, and some original ones. Predefined tests suites for sequences of uniform random numbers over the interval (0, 1) and for bit sequences are available. Tools are also offered to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator's period length, before the generator starts to fail the test systematically. Finally, the library provides various types of generators implemented in generic form, as well as many specific generators proposed in the literature or found in widely used software. The tests can be applied to instances of the generators predefined in the library, or to user-defined generators, or to streams of random numbers produced by any kind of device or stored in files. Besides introducing TestU01, the article provides a survey and a classification of statistical tests for RNGs. It also applies batteries of tests to a long list of widely used RNGs.

972 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023139
2022283
2021173
2020286
2019266
2018247