Generating Random Numbers for Cryptographic Modules Using Race Conditions in GPU

2012 ◽  
pp. 96-102 ◽  
Author(s):  
Yongjin Yeom
Keyword(s):  
Random Numbers ◽  
Race Conditions

Generating True Random Numbers Based on Multicore CPU Using Race Conditions and Chaotic Maps

2020 ◽  
Vol 45 (12) ◽  
pp. 10019-10032
Author(s):  
Je Sen Teh ◽  
Moatsum Alawida ◽  
Azman Samsudin
Keyword(s):  
Chaotic Maps ◽  
Random Numbers ◽  
Race Conditions ◽  
Multicore Cpu

Generation of Random Numbers by Means of Optical Manipulator

2011 ◽  
Vol 43 (8) ◽  
pp. 76-80
Author(s):  
Rostislav M. Mikhersky ◽  
Oleg I. Popov
Keyword(s):  
Random Numbers

scholarly journals A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Micromachines ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Junxiu Liu ◽  
Zhewei Liang ◽  
Yuling Luo ◽  
Lvchen Cao ◽  
Shunsheng Zhang ◽  
...  
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Logistic Map ◽  
Random Number Generator ◽  
Alternative Methods ◽  
Random Numbers ◽  
Number Generator ◽  
Stochastic Computing ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

Random numbers for stochastic simulation

1984 ◽  
Vol 6 (4) ◽  
pp. 5-6 ◽  
Author(s):  
Daniel Guinier
Keyword(s):  
Stochastic Simulation ◽  
Random Numbers

scholarly journals An Integrated Silicon Photonic Chip for Continuous-Variable Quantum Random Numbers Generator Based on Vacuum Fluctuation

2021 ◽  
Vol 1865 (2) ◽  
pp. 022018
Author(s):  
Menglin Zhu ◽  
Xiangyu Wang ◽  
Bingjie Xu ◽  
Song Yu ◽  
Ziyang Chen ◽  
...  
Keyword(s):  
Continuous Variable ◽  
Random Numbers ◽  
Vacuum Fluctuation ◽  
Silicon Photonic

scholarly journals Design of True Random Number Circuit with Controllable Frequency

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1517
Author(s):  
Xinsheng Wang ◽  
Xiyue Wang
Keyword(s):  
Random Number ◽  
Noise Source ◽  
State Of The Art ◽  
Building Blocks ◽  
Encryption Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Random Telegraph Noise ◽  
Telegraph Noise ◽  
Novel Method

True random number generators (TRNGs) have been a research hotspot due to secure encryption algorithm requirements. Therefore, such circuits are necessary building blocks in state-of-the-art security controllers. In this paper, a TRNG based on random telegraph noise (RTN) with a controllable rate is proposed. A novel method of noise array circuits is presented, which consists of digital decoder circuits and RTN noise circuits. The frequency of generating random numbers is controlled by the speed of selecting different gating signals. The results of simulation show that the array circuits consist of 64 noise source circuits that can generate random numbers by a frequency from 1 kHz to 16 kHz.

scholarly journals A Guideline on Pseudorandom Number Generation (PRNG) in the IoT

2021 ◽  
Vol 54 (6) ◽  
pp. 1-38
Author(s):  
Peter Kietzmann ◽  
Thomas C. Schmidt ◽  
Matthias Wählisch
Keyword(s):  
General Purpose ◽  
Packet Transmission ◽  
Limited Resources ◽  
Systematic Evaluation ◽  
The Internet ◽  
Random Numbers ◽  
Essential Input ◽  
Attack Surface ◽  
Pseudorandom Number Generation ◽  
The Internet Of Things

Random numbers are an essential input to many functions on the Internet of Things (IoT). Common use cases of randomness range from low-level packet transmission to advanced algorithms of artificial intelligence as well as security and trust, which heavily rely on unpredictable random sources. In the constrained IoT, though, unpredictable random sources are a challenging desire due to limited resources, deterministic real-time operations, and frequent lack of a user interface. In this article, we revisit the generation of randomness from the perspective of an IoT operating system (OS) that needs to support general purpose or crypto-secure random numbers. We analyze the potential attack surface, derive common requirements, and discuss the potentials and shortcomings of current IoT OSs. A systematic evaluation of current IoT hardware components and popular software generators based on well-established test suits and on experiments for measuring performance give rise to a set of clear recommendations on how to build such a random subsystem and which generators to use.

Multilevel Monte Carlo by using the Halton sequence

2020 ◽  
Vol 26 (3) ◽  
pp. 193-203
Author(s):  
Shady Ahmed Nagy ◽  
Mohamed A. El-Beltagy ◽  
Mohamed Wafa
Keyword(s):  
Monte Carlo ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Computational Cost ◽  
Random Numbers ◽  
Multilevel Monte Carlo ◽  
Halton Sequence ◽  
Random Generator ◽  
Mc Simulation ◽  
Multilevel Monte Carlo Method

AbstractMonte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form. This will increase the convergence and accuracy using the generated different samples in the Multilevel Monte Carlo method (MLMC). We compare algorithms by using a pseudo-random generator and a random generator depending on a Halton sequence. The computational cost for different stochastic differential equations increases in a standard MC technique. It will be highly reduced using a Halton sequence, especially in multiplicative stochastic differential equations.

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