Design and Analysis of Configurable Ring Oscillators for True Random Number Generation Based on Coherent Sampling

2021 ◽  
Vol 14 (2) ◽  
pp. 1-20
Author(s):  
Adriaan Peetermans ◽  
Vladimir Rožić ◽  
Ingrid Verbauwhede
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
Ring Oscillator ◽  
Security Requirements ◽  
Random Number Generators ◽  
High Entropy ◽  
Field Programmable ◽  
Automatic Placement ◽  
Easy Integration

True Random Number Generators (TRNGs) are indispensable in modern cryptosystems. Unfortunately, to guarantee high entropy of the generated numbers, many TRNG designs require a complex implementation procedure, often involving manual placement and routing. In this work, we introduce, analyse, and compare three dynamic calibration mechanisms for the COherent Sampling ring Oscillator based TRNG: GateVar , WireVar , and LUTVar , enabling easy integration of the entropy source into complex systems. The TRNG setup procedure automatically selects a configuration that guarantees the security requirements. In the experiments, we show that two out of the three proposed mechanisms are capable of assuring correct TRNG operation even when an automatic placement is carried out and when the design is ported to another Field-Programmable Gate Array (FPGA) family. We generated random bits on both a Xilinx Spartan 7 and a Microsemi SmartFusion2 implementation that, without post processing, passed the AIS-31 statistical tests at a throughput of 4.65 Mbit/s and 1.47 Mbit/s, respectively.

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 Delay-Based True Random Number Generator in Sub-Nanomillimeter IoT Devices

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 817
Author(s):  
Maulana Randa ◽  
Mohammad Samie ◽  
Ian K. Jennions
Keyword(s):  
Random Number ◽  
Promising Result ◽  
Physical Phenomenon ◽  
Random Number Generator ◽  
Ring Oscillator ◽  
Random Number Generators ◽  
Propagation Delays ◽  
Field Programmable ◽  
Iot Devices ◽  
Non Gaussian

True Random Number Generators (TRNGs) use physical phenomenon as their source of randomness. In electronics, one of the most popular structures to build a TRNG is constructed based on the circuits that form propagation delays, such as a ring oscillator, shift register, and routing paths. This type of TRNG has been well-researched within the current technology of electronics. However, in the future, where electronics will use sub-nano millimeter (nm) technology, the components become smaller and work on near-threshold voltage (NTV). This condition has an effect on the timing-critical circuit, as the distribution of the process variation becomes non-gaussian. Therefore, there is an urge to assess the behavior of the current delay-based TRNG system in sub-nm technology. In this paper, a model of TRNG implementation in sub-nm technology was created through the use of a specific Look-Up Table (LUT) in the Field-Programmable Gate Array (FPGA), known as SRL16E. The characterization of the TRNG was presented and it shows a promising result, in that the delay-based TRNG will work properly, with some constraints in sub-nm technology.

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 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 True-Randomness and Pseudo-Randomness in Ring Oscillator-Based True Random Number Generators

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Nathalie Bochard ◽  
Florent Bernard ◽  
Viktor Fischer ◽  
Boyan Valtchanov
Keyword(s):  
Simulation Model ◽  
Mathematical Analysis ◽  
Random Number ◽  
Statistical Tests ◽  
System Security ◽  
Ring Oscillator ◽  
Post Processing ◽  
Random Number Generators ◽  
Entropy Reduction ◽  
The One

The paper deals with true random number generators employing oscillator rings, namely, with the one proposed by Sunar et al. in 2007 and enhanced by Wold and Tan in 2009. Our mathematical analysis shows that both architectures behave identically when composed of the same number of rings and ideal logic components. However, the reduction of the number of rings, as proposed by Wold and Tan, would inevitably cause the loss of entropy. Unfortunately, this entropy insufficiency is masked by the pseudo-randomness caused by XOR-ing clock signals having different frequencies. Our simulation model shows that the generator, using more than 18 ideal jitter-free rings having slightly different frequencies and producing only pseudo-randomness, will let the statistical tests pass. We conclude that a smaller number of rings reduce the security if the entropy reduction is not taken into account in post-processing. Moreover, the designer cannot avoid that some of rings will have the same frequency, which will cause another loss of entropy. In order to confirm this, we show how the attacker can reach a state where over 25% of the rings are locked and thus completely dependent. This effect can have disastrous consequences on the system security.

scholarly journals Implementation of Hardware-Accelerated Scalable Parallel Random Number Generators

VLSI Design ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
JunKyu Lee ◽  
Gregory D. Peterson ◽  
Robert J. Harrison ◽  
Robert J. Hinde
Keyword(s):  
Monte Carlo ◽  
Reconfigurable Computing ◽  
Random Number ◽  
Random Number Generation ◽  
Random Number Generators ◽  
Fine Grained ◽  
Number Generation ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Parallel Random Number Generators

The Scalable Parallel Random Number Generators (SPRNGs) library is widely used in computational science applications such as Monte Carlo simulations since SPRNG supports fast, parallel, and scalable random number generation with good statistical properties. In order to accelerate SPRNG, we develop a Hardware-Accelerated version of SPRNG (HASPRNG) on the Xilinx XC2VP50 Field Programmable Gate Arrays (FPGAs) in the Cray XD1 that produces identical results. HASPRNG includes the reconfigurable logic for FPGAs along with a programming interface which performs integer random number generation. To demonstrate HASPRNG for Reconfigurable Computing (RC) applications, we also develop a Monte Carlo π-estimator for the Cray XD1. The RC Monte Carlo π-estimator shows a 19.1× speedup over the 2.2 GHz AMD Opteron processor in the Cray XD1. In this paper we describe the FPGA implementation for HASPRNG and a π-estimator example application exploiting the fine-grained parallelism and mathematical properties of the SPRNG algorithm.

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.

Pseudo-random number generation based on digit isolation referenced to entropy buffers

SIMULATION ◽  
2021 ◽  
pp. 003754972110544
Author(s):  
Joseph D. Richardson
Keyword(s):  
Gold Standard ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
The Other ◽  
Double Precision ◽  
Random Number Generators ◽  
Dual Nature ◽  
Pseudo Random Number ◽  
Empirical Performance

Unpredictable pseudo-random number generators (PRNGs) are presented based on dissociated components with only coincidental interaction. The first components involve pointers taken from series of floating point numbers (float streams) arising from arithmetic. The pointers are formed by isolating generalized digits sufficiently far from the most significant digits in the float streams and may be combined into multi-digit pointers. The pointers indicate draw locations from the second component which are entropy decks having one or more cards corresponding to the elements used to assemble random numbers. Like playing cards, decks are cut and riffle-shuffled based on rules using digits appearing in the simulations. The various ordering states of the cards provide entropy to the PRNGs. The dual nature of the PRNGs is novel since they can operate either entirely on pointer variability to fixed decks or on shuffling variability using fixed pointer locations. Each component, pointers and dynamic entropy, is dissociated from the other and independently shown to pass stringent statistical tests with the other held as fixed; a “gold standard” mode involves changing the coincidental interaction between these two strong emulators of randomness by either cutting or shuffling prior to each draw. Gold standard modes may be useful in cryptography and in assessing tests themselves. One PRNG contains [Formula: see text] states in the entropy pool, another generates integers approximately 50% faster than the Advanced Encryption Standard (AES) PRNG with similar empirical performance, and a third generates full double-precision floats at speeds comparable to unsigned integer rates of the AES PRNG.

scholarly journals A Mechanical True Random Number Generator

2021 ◽  
Author(s):  
Nozomi Akashi ◽  
Kohei Nakajima ◽  
Mitsuru Shibayama ◽  
Yasuo Kuniyoshi
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Numerical Algorithms ◽  
Random Number Generation ◽  
Computer Algorithms ◽  
Anosov Flow ◽  
Random Number Generators ◽  
Security Applications ◽  
Spatio Temporal

Abstract Random number generation has become an indispensable part of information processing: it is essential for many numerical algorithms, security applications, and in securing fairness in everyday life. Random number generators (RNG) find application in many devices, ranging from dice and roulette wheels, via computer algorithms, lasers to quantum systems, which inevitably capitalize on their physical dynamics at respective spatio-temporal scales. Herein, to the best of our knowledge, we propose the first mathematically proven true RNG (TRNG) based on a mechanical system, particularly the triple linkage of Thurston and Weeks. By using certain parameters, its free motion has been proven to be an Anosov flow, from which we can show that it has an exponential mixing property and structural stability. We contend that this mechanical Anosov flow can be used as a TRNG, which requires that the random number should be unpredictable, irreproducible, robust against the inevitable noise seen in physical implementations, and the resulting distribution's controllability (an important consideration in practice). We investigate the proposed system's properties both theoretically and numerically based on the above four perspectives. Further, we confirm that the random bits numerically generated pass the standard statistical tests for random bits.

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