The lattice structure of pseudo-random number generators

The pairs, triples, etc. from most congruential pseudo-random number generators are known to lie on a lattice, and the ‘uniformity’ of these lattices is reflected in the quality of the output of the generator. Various characteristics of the lattices have been proposed as summaries of the quality of a generator, including the so-called lattice and spectral tests. This paper exploits the concept of polar lattices to show that these charac­terizations are essentially equivalent, and that they can be found to an approximation sufficient for assessing the quality of the generator without extensive searches. Checking generators is important, for many of those provided on small computers are inadequate for serious work.

In this chapter, the author considers existing methods and means of forming pseudo-random sequences of numbers and also are described the main characteristics of random and pseudorandom sequences of numbers. The main theoretical aspects of the construction of pseudo-random number generators are considered. Classification of pseudorandom number generators is presented. The structures and models of the most popular pseudo-random number generators are considered, the main characteristics of generators that affect the quality of the formation of pseudorandom bit sequences are described. The models of the basic mathematical generators of pseudo-random numbers are considered, and also the principles of building hardware generators are presented.


Author(s):  
Sergii Bilan

The chapter analyzes modern methods for constructing pseudo-random number generators based on cellular automata. Also analyzes the influence of neighborhood forms on the evolution of the functioning of cellular automata, as well as on the quality of the formation of pseudo-random bit sequences. Based on the use of various forms of the neighborhood for the XOR function, the quality of generators was analyzed using graphical tests and NIST tests. As a result of experimental studies, the optimal dimension of cellular automata and the number of heterogeneous cells were determined, which make it possible to obtain a high-quality pseudo-random bit sequence. The obtained results allowed to formulate a method for constructing high-quality pseudo-random number generators based on cellular automata, as well as to determine the necessary initial conditions for generators. The proposed generators allow to increase the length of the repetition period of a pseudo-random bit sequence.


2010 ◽  
Vol 36 (1) ◽  
pp. 57-67 ◽  
Author(s):  
P. C. S. Luizi ◽  
F. R. B. Cruz ◽  
J. van de Graaf

2017 ◽  
Vol 90 (3) ◽  
pp. 1661-1670 ◽  
Author(s):  
Luis Gerardo de la Fraga ◽  
Esteban Torres-Pérez ◽  
Esteban Tlelo-Cuautle ◽  
Cuauhtemoc Mancillas-López

1992 ◽  
Vol 5 (4) ◽  
pp. 291-305 ◽  
Author(s):  
D. Ugrin-Šparac

The renewal process generated by the uniform distribution, when interpreted as a transformation of the uniform distribution into a discrete distribution, gives rise to the question of uniqueness of the inverse image. The paper deals with a particular problem from the described domain, that arose in the construction of a complex stochastic test intended to evaluate pseudo-random number generators. The connection of the treated problem with the question of a unique integral representation of Gamma-function is also mentioned.


2021 ◽  
Vol 13 (2) ◽  
pp. 10-18
Author(s):  
Botond L. Márton ◽  
Dóra Istenes ◽  
László Bacsárdi

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.


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