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.

Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

2021 ◽  
pp. 2102172
Author(s):  
Xuehua Li ◽  
Tommaso Zanotti ◽  
Tao Wang ◽  
Kaichen Zhu ◽  
Francesco Maria Puglisi ◽  
...  
Keyword(s):  
Metal Oxide ◽  
Random Number ◽  
Random Number Generators ◽  
Random Telegraph Noise ◽  
Telegraph Noise

scholarly journals Social Noise: Generating Random Numbers from Twitter Streams

2014 ◽  
Vol 14 (01) ◽  
pp. 1550012
Author(s):  
Norberto Fernández ◽  
Fernando Quintas ◽  
Luis Sánchez ◽  
Jesús Arias
Keyword(s):  
Computer Simulation ◽  
Random Number ◽  
State Of The Art ◽  
Computer Systems ◽  
Statistical Test ◽  
Online Gambling ◽  
Random Numbers ◽  
Physical Processes ◽  
Random Number Generators ◽  
Quantum Phenomenon

Due to the multiple applications of random numbers in computer systems (cryptography, online gambling, computer simulation, etc.) it is important to have mechanisms to generate these numbers. True Random Number Generators (TRNGs) are commonly used for this purpose. TRNGs rely on non-deterministic sources to generate randomness. Physical processes (like noise in semiconductors, quantum phenomenon, etc.) play this role in state of the art TRNGs. In this paper, we depart from previous work and explore the possibility of defining social TRNGs using the stream of public messages of the microblogging service Twitter as randomness source. Thus, we define two TRNGs based on Twitter stream information and evaluate them using the National Institute of Standards and Technology (NIST) statistical test suite. The results of the evaluation confirm the feasibility of the proposed approach.

scholarly journals Enhancing the operational efficiency of quantum random number generators

2021 ◽  
Vol 13 (2) ◽  
pp. 10-18
Author(s):  
Botond L. Márton ◽  
Dóra Istenes ◽  
László Bacsárdi
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Practical Implementation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Original Source ◽  
Negative Effect ◽  
Quantum Phenomena ◽  
The Right

Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.

A General-Purpose Uniform Random Number Package

2021 ◽  
pp. 164-176
Author(s):  
E. Jack Chen
Keyword(s):  
Computer Simulation ◽  
Random Number ◽  
General Purpose ◽  
Random Numbers ◽  
Pseudorandom Generator ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Modeling And Analysis ◽  
Generating Sequences ◽  
Simulation Systems

As computer capacities and simulation technologies advance, simulation has become the method of choice for modeling and analysis. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Simulation studies are typically preceded by transforming in a more or less complicated way of a sequence of numbers between 0 and 1 produced by a pseudorandom generator into an observation of the measure of interest. Random numbers are a fundamental resource in science and technology. A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, random number generators also play an important role in cryptography and in the blockchain ecosystem. All samples of the sequence are generated independently of each other, and the value of the next sample in the sequence cannot be predicted, regardless of how many samples have already been produced.

scholarly journals A Survey on True Random Number Generators Based on Chaos

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Yu ◽  
Lixiang Li ◽  
Qiang Tang ◽  
Shuo Cai ◽  
Yun Song ◽  
...  
Keyword(s):  
Chaotic System ◽  
Key Management ◽  
Random Number ◽  
Initial Conditions ◽  
Rapid Development ◽  
Current Mode ◽  
Random Numbers ◽  
Random Number Generators ◽  
Network Information ◽  
Survey Paper

With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

scholarly journals On the effects of pseudorandom and quantum-random number generators in soft computing

2019 ◽  
Vol 24 (12) ◽  
pp. 9243-9256
Author(s):  
Jordan J. Bird ◽  
Anikó Ekárt ◽  
Diego R. Faria
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Random Forest ◽  
Soft Computing ◽  
Convolutional Neural Networks ◽  
Random Number ◽  
Random Tree ◽  
Random Numbers ◽  
Random Number Generators ◽  
Eeg Classification

Abstract In this work, we argue that the implications of pseudorandom and quantum-random number generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in soft computing until this work. We use a CPU and a QPU to generate random numbers for multiple machine learning techniques. Random numbers are employed in the random initial weight distributions of dense and convolutional neural networks, in which results show a profound difference in learning patterns for the two. In 50 dense neural networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at + 0.1%, and QRNG exceeded PRNG for mental state EEG classification by + 2.82%. In 50 convolutional neural networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10 problems are benchmarked, and in MNIST the QRNG experiences a higher starting accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by + 0.92%. The n-random split of a Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification data sets, a QRT seemed inferior to a RT as it performed on average worse by − 0.12%. This pattern is also seen in the EEG classification problem, where a QRT performs worse than a RT by − 0.28%. Finally, the QRT is ensembled into a Quantum Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF). Ten to 100 ensembles of trees are benchmarked for the accent and EEG classification problems. In accent classification, the best RF (100 RT) outperforms the best QRF (100 QRF) by 0.14% accuracy. In EEG classification, the best RF (100 RT) outperforms the best QRF (100 QRT) by 0.08% but is extremely more complex, requiring twice the amount of trees in committee. All differences are observed to be situationally positive or negative and thus are likely data dependent in their observed functional behaviour.

GENERATING PARALLEL RANDOM NUMBER GENERATORS BY CELLULAR PROGRAMMING

1996 ◽  
Vol 07 (02) ◽  
pp. 181-190 ◽  
Author(s):  
MOSHE SIPPER ◽  
MARCO TOMASSINI
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Programming Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Parallel Random Number Generators

Random numbers are needed in a variety of applications, yet finding good random number generators is a difficult task. In this paper non-uniform cellular automata (CA) are studied, presenting the cellular programming algorithm for co-evolving such CAs to perform computations. The algorithm is applied to the evolution of random number generators; our results suggest that evolved generators are at least as good as previously described CAs, with notable advantages arising from the existence of a "tunable" algorithm for obtaining random number generators.

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.

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.

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