scholarly journals SOURCES OF RANDOMNESS FOR USE IN RANDOM NUMBER GENERATION

2014 ◽  
pp. 54-60
Author(s):  
A. G. Fragopoulos ◽  
D. N. Serpanos
Keyword(s):  
Embedded Systems ◽  
Significant Role ◽  
Random Number ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
High Entropy ◽  
Seed Sources ◽  
Constrained Environments ◽  
Specific Sequences

Efficient generation of random numbers plays significant role in cryptographic applications. Such a generator has to produce unpredictable and un-correlated random bits. Random number generators are classified as pseudo-random number generators (PRNGs) and true random number generators (TRNGs). The first ones have the disadvantage that they can be proven predictable, while the latter ones can produce true random bits but it is not easy to re-produce specific sequences or implement them in constrained environments and there may exist correlations and biases of produced sequences. A third class of random number generators has been introduced, called hybrid-random number generators (h-RNGs), where there is a combination of a cryptographically strong PRNGs or TRNGs which are seeded, and possibly re-seeded, through a source of randomness with high entropy. In this paper, we present an overview of various sources of randomness that can be used either as direct random number generators or as seed sources in h-RNGs, for application in embedded systems.

scholarly journals Sensor-Seeded Cryptographically Secure Random Number Generation

2019 ◽  
Vol 8 (3) ◽  
pp. 1854-1857
Keyword(s):  
Random Number ◽  
Random Sequence ◽  
Random Number Generation ◽  
Sensor Data ◽  
Random Numbers ◽  
Pseudorandom Number Generator ◽  
Random Number Generators ◽  
Secret Keys ◽  
Initialization Vector ◽  
Entropy Source

Random numbers are essential to generate secret keys, initialization vector, one-time pads, sequence number for packets in network and many other applications. Though there are many Pseudo Random Number Generators available they are not suitable for highly secure applications that require high quality randomness. This paper proposes a cryptographically secure pseudorandom number generator with its entropy source from sensor housed on mobile devices. The sensor data are processed in 3-step approach to generate random sequence which in turn fed to Advanced Encryption Standard algorithm as random key to generate cryptographically secure random numbers.

Uniform Random Number Generation With Jumping Facilities

2018 ◽  
pp. 1297-1306
Author(s):  
E. Jack Chen
Keyword(s):  
Variance Reduction ◽  
Random Number ◽  
Simple Procedure ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Number Generation ◽  
Multiple Recursive Generator ◽  
Generating Sequences

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined Multiple Recursive Generator) combines two multiple recursive random number generators with a period length of approximately 2191 (˜ 3.1× 1057), good speed, and excellent theoretical properties.

scholarly journals Quantum random number cloud platform

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Leilei Huang ◽  
Hongyi Zhou ◽  
Kai Feng ◽  
Chongjin Xie
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Cloud Services ◽  
Random Numbers ◽  
Cloud Platform ◽  
Authentication Protocols ◽  
Quantum Devices ◽  
Random Number Generators ◽  
Different Types ◽  
Cloud Servers

AbstractRandomness lays the foundation for information security. Quantum random number generation based on various quantum principles has been proposed to provide true randomness in the last two decades. We integrate four different types of quantum random number generators on the Alibaba Cloud servers to enhance cybersecurity. Post-processing modules are integrated into the quantum platform to extract true random numbers. We employ improved authentication protocols where original pseudo-random numbers are replaced with quantum ones. Users from the Alibaba Cloud, such as Ant Financial and Smart Access Gateway, request random numbers from the quantum platform for various cryptographic tasks. For cloud services demanding the highest security, such as Alipay at Ant Financial, we combine the random numbers from four quantum devices by XOR the outputs to enhance practical security. The quantum platform has been continuously run for more than a year.

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.

Uniform Random Number Generation With Jumping Facilities

2019 ◽  
pp. 337-349
Author(s):  
E. Jack Chen
Keyword(s):  
Variance Reduction ◽  
Random Number ◽  
Simple Procedure ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Number Generation ◽  
Multiple Recursive Generator ◽  
Generating Sequences

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined multiple recursive generator) combines two multiple recursive random number generators with a period length of approximately 2191 (≈ 3.1× 1057), good speed, and excellent theoretical properties.

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

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.

scholarly journals Evaluation of Random Number Generator Functions Using Statistical Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-5
Author(s):  
Rajashree Chaurasia
Keyword(s):  
Programming Languages ◽  
Goodness Of Fit ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
The Third

Most programming languages have in-built functions for the sole purpose of generating pseudo-random numbers. This manuscript is aimed at analyzing the appropriateness of some of these in-built functions for some basic goodness-of-fit statistical tests for random number generators. The document is divided into four sections. The first section gives a broad introduction about randomness and the methods of generation of pseudo-random numbers. Section two discusses the statistical tests that were employed for testing the built-in library functions for random number generation. This section is followed by an analysis of the data collected for the various statistics in the third section, and lastly, the fourth section presents the results of the data analysis.

scholarly journals Exploring the stochasticity of chemical processes in an automated robotic crystallization platform to generate random numbers

2019 ◽  
Author(s):  
Leroy Cronin ◽  
Edward Lee ◽  
Juan Manuel Parrilla Gutiérrez ◽  
Alon Henson ◽  
Euan K. Brechin
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Binary Sequences ◽  
Random Numbers ◽  
Deterministic System ◽  
One Pot ◽  
Random Number Generators ◽  
Mersenne Twister ◽  
Automated Platform ◽  
Binarization Algorithm

<p>Random number generators are important in fields which require non-deterministic input, such as cryptography. One example of a non-deterministic system is found in chemistry via the crystallization of chemical compounds, which occurs through stochastic processes. Herein, we present an automated platform capable of generating random numbers from observation of crystallizations resulting from multiple parallel one-pot chemical reactions. From the resulting images, crystals were identified using computer vision, and binary sequences were obtained by applying a binarization algorithm to these regions. An assessment of randomness of these sequences was undertaken by applying a barrage of tests for randomness described by the National Institute of Standards and Technology (NIST). We find that numbers generated through this method are able to pass each of the three levels for each of the NIST tests. We then compare the encryption strength of the random numbers generated from each of the crystallizing systems to that of a pseudo-random number generation algorithm (the Mersenne Twister). We find that messages encrypted using chemically derived random numbers take significantly longer to decrypt than the algorithmically generated number.</p>

scholarly journals Exploring the stochasticity of chemical processes in an automated robotic crystallization platform to generate random numbers

2019 ◽  
Author(s):  
Leroy Cronin ◽  
Edward Lee ◽  
Juan Manuel Parrilla Gutiérrez ◽  
Alon Henson ◽  
Euan K. Brechin
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Binary Sequences ◽  
Random Numbers ◽  
Deterministic System ◽  
One Pot ◽  
Random Number Generators ◽  
Mersenne Twister ◽  
Automated Platform ◽  
Binarization Algorithm

<p>Random number generators are important in fields which require non-deterministic input, such as cryptography. One example of a non-deterministic system is found in chemistry via the crystallization of chemical compounds, which occurs through stochastic processes. Herein, we present an automated platform capable of generating random numbers from observation of crystallizations resulting from multiple parallel one-pot chemical reactions. From the resulting images, crystals were identified using computer vision, and binary sequences were obtained by applying a binarization algorithm to these regions. An assessment of randomness of these sequences was undertaken by applying a barrage of tests for randomness described by the National Institute of Standards and Technology (NIST). We find that numbers generated through this method are able to pass each of the three levels for each of the NIST tests. We then compare the encryption strength of the random numbers generated from each of the crystallizing systems to that of a pseudo-random number generation algorithm (the Mersenne Twister). We find that messages encrypted using chemically derived random numbers take significantly longer to decrypt than the algorithmically generated number.</p>

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