Random Number Generation with Entropy Sources in the Graphics Processing Units

2012 ◽  
Vol 268-270 ◽  
pp. 1863-1868
Author(s):  
Yong Jin Yeom
Keyword(s):  
Graphics Processing Units ◽  
Secure Communication ◽  
Random Number ◽  
Random Number Generator ◽  
Binary Sequence ◽  
Random Number Generation ◽  
Random Number Generators ◽  
Computing Environments ◽  
Graphics Processing ◽  
Modern Cryptography

The random number generator (RNG) is indispensable to modern cryptography since cryptographic services make use of random numbers for deriving encryption keys or nonces in protocols for secure communication. Operating systems like Linux and Windows provide built-in random number generators which can be accessed by cryptographic modules and other processes. If the system fails to collect sufficient entropy from the operating environment, the output from the RNG is blocked or becomes less secure. In this paper, we propose a method providing sufficient entropy to RNGs using graphics processing units. By estimating run-time of the kernel function in GPU, we can gather noisy data with bias. After the distillation process, we obtain a binary sequence for entropy input to the deterministic part of RNG. Our scheme was implemented on the computing environments using NVIDIA’s GPU GTX 580 and GTX 610M.

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 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 Novel Certification Method for Quantum Random Number Generators

2021 ◽  
Vol 13 (3) ◽  
pp. 28-38
Author(s):  
Maksim Iavich ◽  
◽  
Tamari Kuchukhidze ◽  
Sergiy Gnatyuk ◽  
Andriy Fesenko
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Advantages And Disadvantages ◽  
Simulation Techniques ◽  
Self Testing ◽  
Certification Method

Random numbers have many uses, but finding true randomness is incredibly difficult. Therefore, quantum mechanics is used, using the essentially unpredictable behavior of a photon, to generate truly random numbers that form the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the performance of our device and the quality of the random bits generated. Self-testing as well as device independent quantum random number generation methods are analyzed in the paper. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. The method is very important for computer science, because it combines the best features from selftesting and device independent methods. It can be used, when the random numbers’ entropy depends on the device and when it does not. In the related researches, these approaches are offered to be used separately, depending on the random number generator. The offered novel certification technology can be properly used, when the device is compromised or spoiled. The technology can successfully detect unintended irregularities, operational problems, abnormalities and problems in the randomization process. The offered mythology assists to eliminate problems related to physical devices. The offered system has the higher certification randomness security and is faster than self-testing approaches. The method is rather efficient because it implements the different certification approaches in the parallel threads. The offered techniques make the offered research must more efficient than the other existing approaches. The corresponding programming simulation is implemented by means of the simulation techniques.

Digital-Analog Hybrid Scheme and Its Application to Chaotic Random Number Generators

2017 ◽  
Vol 27 (14) ◽  
pp. 1750210 ◽  
Author(s):  
Zeshi Yuan ◽  
Hongtao Li ◽  
Yunchi Miao ◽  
Wen Hu ◽  
Xiaohua Zhu
Keyword(s):  
Graphics Processing Units ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Main Body ◽  
Hybrid Scheme ◽  
Memory Element ◽  
Finite Precision ◽  
Practical Applications ◽  
Field Programmable

Practical random number generation (RNG) circuits are typically achieved with analog devices or digital approaches. Digital-based techniques, which use field programmable gate array (FPGA) and graphics processing units (GPU) etc. usually have better performances than analog methods as they are programmable, efficient and robust. However, digital realizations suffer from the effect of finite precision. Accordingly, the generated random numbers (RNs) are actually periodic instead of being real random. To tackle this limitation, in this paper we propose a novel digital-analog hybrid scheme that employs the digital unit as the main body, and minimum analog devices to generate physical RNs. Moreover, the possibility of realizing the proposed scheme with only one memory element is discussed. Without loss of generality, we use the capacitor and the memristor along with FPGA to construct the proposed hybrid system, and a chaotic true random number generator (TRNG) circuit is realized, producing physical RNs at a throughput of Gbit/s scale. These RNs successfully pass all the tests in the NIST SP800-22 package, confirming the significance of the scheme in practical applications. In addition, the use of this new scheme is not restricted to RNGs, and it also provides a strategy to solve the effect of finite precision in other digital systems.

COMMUNICATION BETWEEN SYNCHRONIZED RANDOM NUMBER GENERATORS

2004 ◽  
Vol 14 (11) ◽  
pp. 3995-4008 ◽  
Author(s):  
WEIGUANG YAO ◽  
PEI YU ◽  
CHRISTOPHER ESSEX
Keyword(s):  
Chaotic System ◽  
Secure Communication ◽  
Random Number ◽  
Lorenz System ◽  
Random Number Generator ◽  
Number Generator ◽  
Random Number Generators ◽  
The Lorenz System ◽  
Chaotic Carrier

In most published chaos-based communication schemes, the system's parameters used as a key could be intelligently estimated by a cracker based on the fact that information about the key is contained in the chaotic carrier. In this paper, we will show that the least significant digits (LSDs) of a signal from a chaotic system can be so highly random that the system can be used as a random number generator. Secure communication could be built between the synchronized generators nonetheless. The Lorenz system is used as an illustration.

scholarly journals A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 618 ◽  
Author(s):  
Min Huang ◽  
Ziyang Chen ◽  
Yichen Zhang ◽  
Hong Guo
Keyword(s):  
Shot Noise ◽  
Distribution System ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Continuous Variable ◽  
Number Generator ◽  
Sampling Device ◽  
Random Number Generators ◽  
The Impact

Among all the methods of extracting randomness, quantum random number generators are promising for their genuine randomness. However, existing quantum random number generator schemes aim at generating sequences with a uniform distribution, which may not meet the requirements of specific applications such as a continuous-variable quantum key distribution system. In this paper, we demonstrate a practical quantum random number generation scheme directly generating Gaussian distributed random sequences based on measuring vacuum shot noise. Particularly, the impact of the sampling device in the practical system is analyzed. Furthermore, a related post-processing method, which maintains the fine distribution and autocorrelation properties of raw data, is exploited to extend the precision of generated Gaussian distributed random numbers to over 20 bits, making the sequences possible to be utilized by the following system with requiring high precision numbers. Finally, the results of normality and randomness tests prove that the generated sequences satisfy Gaussian distribution and can pass the randomness testing well.

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