scholarly journals Generation of an EDS Key Based on a Graphic Image of a Subject’s Face Using the RC4 Algorithm

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
Alexey Semenkov ◽  
Dmitry Bragin ◽  
Yakov Usoltsev ◽  
Anton Konev ◽  
Evgeny Kostuchenko
Keyword(s):  
Image Recognition ◽  
Random Number ◽  
Statistical Test ◽  
The Other ◽  
Test Results ◽  
Random Generation ◽  
Facial Image ◽  
Random Number Generators ◽  
Graphic Image ◽  
High Level

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user’s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole.

scholarly journals A New Statistical Test for PRNG Based on the Attendance’s Law

2021 ◽  
pp. 37-46
Author(s):  
Babacar Alasane Ndaw ◽  
Ousmane Ndiaye ◽  
Mamadou Sanghar´e ◽  
Cheikh Thi´ecoumba Gueye
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Deterministic Algorithm ◽  
Statistical Test ◽  
The Other ◽  
Cryptographic Primitives ◽  
Random Number Generators ◽  
Other Hand ◽  
The One

One family of the cryptographic primitives is random Number Generators (RNG) which have several applications in cryptography such that password generation, nonce generation, Initialisation vector for Stream Cipher, keystream. Recently they are also used to randomise encryption and signature schemes. A pseudo-random number generator (PRNG) or a pseudo-random bit generator (PRBG) is a deterministic algorithm that produces numbers whose distribution is on the one hand indistinguishable from uniform ie. that the probabilities of appearance of the different symbols are equal and that these appearances are all independent. On the other hand, the next output of a PRNG must be unpredictable from all its previous outputs. Indeed, A set of statistical tests for randomness has been proposed in the literature and by NIST to evaluate the security of random(pseudo) bit or block. Unfortunately there are non-random binary streams that pass these standardized tests. In this pap er, as outcome, we intro duce on the one hand a new statistical test in a static contextcalled attendance’s law and on the other hand a distinguisher based on this new attendance’s law.    

scholarly journals On pseudorandom number generators

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 128
Author(s):  
Daniel Chicayban Bastos ◽  
Luis Antonio Brasil Kowada ◽  
Raphael C. S. Machado
Keyword(s):  
Programming Languages ◽  
Random Number ◽  
Statistical Tests ◽  
Statistical Test ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Statistical Sampling ◽  
Simple Task ◽  
Random Number Generators ◽  
Pseudorandom Number Generators

<p class="Abstract">Statistical sampling and simulations produced by algorithms require fast random number generators; however, true random number generators are often too slow for the purpose, so pseudorandom number generators are usually more suitable. But choosing and using a pseudorandom number generator is no simple task; most pseudorandom number generators fail statistical tests. Default pseudorandom number generators offered by programming languages usually do not offer sufficient statistical properties. Testing random number generators so as to choose one for a project is essential to know its limitations and decide whether the choice fits the project’s objectives. However, this study presents a reproducible experiment that demonstrates that, despite all the contributions it made when it was first published, the popular NIST SP 800-22 statistical test suite as implemented in the software package is inadequate for testing generators.</p>

scholarly journals High-Density Physical Random Number Generator Using Spin Signals in Multidomain Ferromagnetic Layer

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Sungwoo Chun ◽  
Seung-Beck Lee ◽  
Masahiko Hara ◽  
Wanjun Park ◽  
Song-Ju Kim
Keyword(s):  
Room Temperature ◽  
Random Number ◽  
Random Number Generator ◽  
Ferromagnetic Layer ◽  
Magnetic Tunnel Junction ◽  
Statistical Test ◽  
High Density ◽  
Number Generator ◽  
Test Results ◽  
Applied Current

A high-density random number generator (RNG) based on spin signals in a multidomain ferromagnetic layer in a magnetic tunnel junction (MTJ) is proposed and fabricated. Unlike conventional spin-based RNGs, the proposed method does not require one to control an applied current, leading to a time delay in the system. RNG demonstrations are performed at room temperature. The randomness of the bit sequences generated by the proposed RNG is verified using the FIPS 140-2 statistical test suite provided by the NIST. The test results validate the effectiveness of the proposed RNGs. Our results suggest that we can obtain high-density, ultrafast RNGs if we can achieve high integration on the chip.

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 Random Number Generators Based on EEG Non-linear and Chaotic Characteristics

2017 ◽  
Author(s):  
Dang Nguyen ◽  
Dat Tran ◽  
Wanli Ma ◽  
Dharmendra Sharma
Keyword(s):  
Random Number ◽  
Brain Activity ◽  
Eeg Signal ◽  
Success Rates ◽  
Random Number Generators ◽  
Non Linear ◽  
Number Sequences ◽  
Level Of Significance ◽  
High Level

Current electroencephalogram (EEG)-based methods in security have been mainly used for person authentication and identification purposes only. The non-linear and chaotic characteristics of EEG signal have not been taken into account. In this paper, we propose a new method that explores the use of these EEG characteristics in generating random numbers. EEG signal and its wavebands are transformed into bit sequences that are used as random number sequences or as seeds for pseudo-random number generators. EEG signal has the following advantages: 1) it is noisy, complex, chaotic and non-linear in nature, 2) it is very difficult to mimic because similar mental tasks are person dependent, and 3) it is almost impossible to steal because the brain activity is sensitive to the stress and the mood of the person and an aggressor cannot force the person to reproduce his/her mental pass-phrase. Our experiments were conducted on the four EEG datasets: AEEG, Alcoholism, DEAP and GrazA 2008. The randomness of the generated bit sequences was tested at a high level of significance by comprehensive battery of tests recommended by the National Institute of Standard and Technology (NIST) to verify the quality of random number generators, especially in cryptography application. Our experimental results showed high average success rates for all wavebands and the highest rate is 99.17% for the gamma band.  

scholarly journals Testing a hardware random number generator using NIST statistical test suite

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (4) ◽  
pp. 37-42
Author(s):  
M. O. Pikuza ◽  
S. Yu. Mikhnevich
Keyword(s):  
Data Acquisition ◽  
Random Number ◽  
Random Sequence ◽  
Random Number Generator ◽  
Reverse Current ◽  
Statistical Test ◽  
Test Suite ◽  
Number Generator ◽  
Random Number Generators ◽  
Output Sequence

Random number generators are required for the operation of cryptographic information protection systems. For а correct application of the generator in the field of information security, it is necessary that its output sequence to be indistinguishable from a uniformly distributed random sequence. To verify this, it is necessary to test the generator output sequence using various statistical test suites such as Dihard and NIST. The purpose of this work is to test a prototype hardware random number generator. The generator is built on the basis of the ND103L noise diode and has a random digital sequence of binary numbers at the output. In the prototype there is a possibility of regulating the amount of reverse current through the noise diode, as well as setting the data acquisition period, i.e. data generation frequency. In the course of operation, a number of sequences of random numbers were removed from the generator at various values of the reverse current through the noise diode, the period of data acquisition and the ambient temperature. The resulting sequences were tested using the NIST statistical test suite. After analyzing the test results, it was concluded that the generator operates relatively stably in a certain range of initial parameters, while the deterioration in the quality of the generator's operation outside this range is associated with the technical characteristics of the noise diode. It was also concluded that the generator under study is applicable in certain applications and to improve the stability of its operation, it can be improved both in hardware and software. The results of this work can be useful to developers of hardware random number generators built according to a similar scheme.

Generating High-Quality Random Numbers by Next Nearest-Neighbor Cellular Automata

2013 ◽  
Vol 765-767 ◽  
pp. 1200-1204 ◽  
Author(s):  
Ping Ping ◽  
Feng Xu ◽  
Zhi Jian Wang
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Random Number ◽  
Nearest Neighbor ◽  
Random Sequence ◽  
Statistical Test ◽  
Random Numbers ◽  
High Quality ◽  
Random Number Generators ◽  
Nearest Neighborhood

Cellular automaton (CA) has been widely investigated as random number generators (RNGs). However, the CA rule and the number of neighbors must be chosen carefully for good randomness. In Ref. [11], non-uniform CA with next nearest neighborhood was applied to generate a pseudo-random sequence. Considering that non-uniform CA has more complex implementation in hardware and needs lager memory to store different rules than uniform CA. In this paper, we propose new RNGs based on uniform CA with next nearest neighborhood. Time spacing technique and NIST statistical test suite are used to find optimal rules for uniform CA. Experiment results show that the sequences generated by uniform CA with optimal rules successfully passed all tests in the NIST suite.

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 PROPOSED APPROACH FOR IMPROVING RNA CRYPTO-KEY BASED ON POLYNOMIAL CONVOLUTION

2018 ◽  
Vol 44 (1) ◽  
pp. 6-10
Author(s):  
Estabraq Abdulredaa Kadhim
Keyword(s):  
Random Number ◽  
The State ◽  
Key Generation ◽  
Random Number Generators ◽  
High Level

Random Number Generators are fundamental toolsfor cryptography protocols and algorithms. The basic problems thatface any crypto key generator are randomness, correlations anddistribution of the state of key sequence. This paper proposed a newmethod to enhance RNA crypto key generation. It has beenimplemented by extending the crypto key by applying polynomialconvolution technique which extracts the mask filter from the sameRNA key sequence depending on the start and end codon properties.This will provide another high level of extension and generaterandom-strength crypto key. The proposal approach could passthrough the statistical measurements successfully and achieved highrate of randomness (approximated to 96%).

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