scholarly journals Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations

2021 ◽  
Vol 11 (16) ◽  
pp. 7413
Author(s):  
Maurício J. Ferreira ◽  
Nuno A. Silva ◽  
Armando N. Pinto ◽  
Nelson J. Muga
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Excess Entropy ◽  
Random Number Generator ◽  
Estimation Method ◽  
Local Oscillator ◽  
Vacuum Fluctuations ◽  
Random Number Generators ◽  
The Impact

Quantum random number generators (QRNGs) are currently in high demand across a large number of cryptographic applications as secure sources of true randomness. In this work, we characterize the conditions from which randomness can be extracted in a QRNG based on homodyne measurements of vacuum fluctuations by assessing the impact of experimental limitations, such as the digitizer resolution or the presence of excess local oscillator (LO) noise due to an unbalanced detection. Moreover, we propose an estimation method to quantify the excess entropy contribution introduced by an unbalanced detection and analyze the implementation of the post-processing algorithm. Finally, we submitted the generated numbers to a set of statistical tests to assess the quality of its output randomness and verified that it passes the standard libraries.

SRAM Based Random Number Generator for Non-Repeating Pattern Generation

2014 ◽  
Vol 573 ◽  
pp. 181-186 ◽  
Author(s):  
G.P. Ramesh ◽  
A. Rajan
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Pattern Generation ◽  
Storage Device ◽  
Number Generator ◽  
Random Number Generators ◽  
Field Programmable ◽  
The Look

—Field-programmable gate array (FPGA) optimized random number generators (RNGs) are more resource-efficient than software-optimized RNGs because they can take advantage of bitwise operations and FPGA-specific features. A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. The many applications of randomness have led to the development of several different methods for generating random data. Several computational methods for random number generation exist, but often fall short of the goal of true randomness though they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible).LUT-SR Family of Uniform Random Number Generators are able to handle randomness only based on seeds that is loaded in the look up table. To make random generation efficient, we propose new approach based on SRAM storage device.Keywords: RNG, LFSR, SRAM

scholarly journals Pseudo-Random Number Generator Based on Logistic Chaotic System

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 960 ◽  
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.

A CHAOS-BASED RANDOM NUMBER GENERATOR FOR EIGHT-BIT MICRO-CONTROLLER SYSTEM

2008 ◽  
Vol 18 (03) ◽  
pp. 851-867 ◽  
Author(s):  
K. W. TANG ◽  
H. S. KWOK ◽  
WALLACE K. S. TANG ◽  
K. F. MAN
Keyword(s):  
Chaotic Systems ◽  
Random Number ◽  
Statistical Tests ◽  
Chaotic Map ◽  
Random Number Generator ◽  
High Dimensional ◽  
Number Generator ◽  
Constrained System ◽  
Random Number Generators ◽  
Consumer Markets

Random number generators are widely used in different applications. However, it is difficult to obtain a good random number generator in low precision and resource constrained system, such as an eight-bit micro-controller system which is still commonly used in industrial and consumer markets. This paper provides a practical solution for this problem based on chaotic systems. By the use of a modified Chua's circuit, it is demonstrated that the sampled state, after post-processing by a high-dimensional chaotic map, can be used as a random source even in an eight-bit environment. The randomness of the generated sequence is testified and confirmed by different statistical tests and the up-to-date statistical suite.

scholarly journals Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

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.

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

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.    

scholarly journals Novel Memristor Based True Random Number Generator

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.

scholarly journals Developing pseudo random number generator based on neural networks and neurofuzzy systems

2021 ◽  
Author(s):  
Kayvan Tirdad
Keyword(s):  
Neural Networks ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Hopfield Neural Networks ◽  
Number Generator ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Neurofuzzy Systems ◽  
Pseudo Random Number Generator

Pseudo random number generators (PRNGs) are one of the most important components in security and cryptography applications. We propose an application of Hopfield Neural Networks (HNN) as pseudo random number generator. This research is done based on a unique property of HNN, i.e., its unpredictable behavior under certain conditions. Also, we propose an application of Fuzzy Hopfield Neural Networks (FHNN) as pseudo random number generator. We compare the main features of ideal random number generators with our proposed PRNGs. We use a battery of statistical tests developed by National Institute of Standards and Technology (NIST) to measure the performance of proposed HNN and FHNN. We also measure the performance of other standard PRNGs and compare the results with HNN and FHNN PRNG. We have shown that our proposed HNN and FHNN have good performance comparing to other PRNGs accordingly.

Analysis of Distributed Token Circulation Algorithm with Faulty Random Number Generator

2014 ◽  
Vol 24 (01) ◽  
pp. 1450002
Author(s):  
Shinji Kawai ◽  
Fukuhito Ooshita ◽  
Hirotsugu Kakugawa ◽  
Toshimitsu Masuzawa
Keyword(s):  
Distributed Computing ◽  
Random Number ◽  
Random Number Generator ◽  
Convergence Time ◽  
Number Generator ◽  
Ring Networks ◽  
Random Number Generators ◽  
New Class ◽  
Token Circulation ◽  
The Impact

Randomization is a technique to improve efficiency and computability of distributed computing. In this paper, we investigate fault tolerance of distributed computing against faults of random number generators. We introduce an RNG (Random Number Generator)-fault as a new class of faults; a random number generator on an RNG-faulty process outputs the same number deterministically. This paper is the first work that considers faults of randomness in distributed computing. We investigate the role of randomization by observing the impact of RNG-faults on performance of a self-stabilizing token circulation algorithm on unidirectional n-node ring networks. In the analysis, we assume there exist nf (0 ≤ nf ≤ n−1) RNG-faulty nodes and each RNG-faulty node always transfers a token to the next node. Our results are threefold: (1) We derive the upper bound on the expected convergence time in the case of nf = n − 1. (2) Our simulation result shows that the expected convergence time is maximum when nf = n − 1. (3) We derive the expected token circulation time for each nf (0 ≤ nf ≤ n − 1).

scholarly journals Developing pseudo random number generator based on neural networks and neurofuzzy systems

2021 ◽  
Author(s):  
Kayvan Tirdad
Keyword(s):  
Neural Networks ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Hopfield Neural Networks ◽  
Number Generator ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Neurofuzzy Systems ◽  
Pseudo Random Number Generator

Pseudo random number generators (PRNGs) are one of the most important components in security and cryptography applications. We propose an application of Hopfield Neural Networks (HNN) as pseudo random number generator. This research is done based on a unique property of HNN, i.e., its unpredictable behavior under certain conditions. Also, we propose an application of Fuzzy Hopfield Neural Networks (FHNN) as pseudo random number generator. We compare the main features of ideal random number generators with our proposed PRNGs. We use a battery of statistical tests developed by National Institute of Standards and Technology (NIST) to measure the performance of proposed HNN and FHNN. We also measure the performance of other standard PRNGs and compare the results with HNN and FHNN PRNG. We have shown that our proposed HNN and FHNN have good performance comparing to other PRNGs accordingly.

scholarly journals Difusi Bebas 1D dan 2D dengan Monte Carlo: Perbandingan Distribusi Bilangan Random Normal dan Seragam dengan Box-Müller

2021 ◽  
Vol 9 (1) ◽  
pp. 55-64
Author(s):  
Fairusy Fitria Haryani ◽  
◽  
Freddy Haryanto ◽  
Sparisoma Viridi ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Particle Distribution ◽  
Random Number Generator ◽  
Particle Interaction ◽  
Number Generator ◽  
Random Number Generators ◽  
High Concentrations ◽  
The Impact ◽  
Mc Method

Many biological processes in the human body are based on the diffusion system. Diffusion is defined as a process of random movement of the particle whose the direction is from high concentrations to low concentrations. Many of various study of diffusion have been done both experimentally and computationally. Because the particle interaction is stochastic, the Monte Carlo (MC) method is used in performing particle simulations. The main of MC method is the use of random numbers. Many software have provided uniform random number generators. But based on the analytic results, the solution is normal distribution. Therefore, Box-Müller can be used as a transformation of particle distribution. The software used, MATLAB, has a normal random generator. Therefore, the aims of this study is comparing particle distribution of these two different random number generator with MATLAB and showing the impact of timestep parameter to these random number generator. This result can be used as based for the modelling of more complex biological systems.

Sign in / Sign up

Export Citation Format

Share Document