4,273 research outputs found
A Comparative Study of Some Pseudorandom Number Generators
We present results of an extensive test program of a group of pseudorandom
number generators which are commonly used in the applications of physics, in
particular in Monte Carlo simulations. The generators include public domain
programs, manufacturer installed routines and a random number sequence produced
from physical noise. We start by traditional statistical tests, followed by
detailed bit level and visual tests. The computational speed of various
algorithms is also scrutinized. Our results allow direct comparisons between
the properties of different generators, as well as an assessment of the
efficiency of the various test methods. This information provides the best
available criterion to choose the best possible generator for a given problem.
However, in light of recent problems reported with some of these generators, we
also discuss the importance of developing more refined physical tests to find
possible correlations not revealed by the present test methods.Comment: University of Helsinki preprint HU-TFT-93-22 (minor changes in Tables
2 and 7, and in the text, correspondingly
Guaranteeing the diversity of number generators
A major problem in using iterative number generators of the form
x_i=f(x_{i-1}) is that they can enter unexpectedly short cycles. This is hard
to analyze when the generator is designed, hard to detect in real time when the
generator is used, and can have devastating cryptanalytic implications. In this
paper we define a measure of security, called_sequence_diversity_, which
generalizes the notion of cycle-length for non-iterative generators. We then
introduce the class of counter assisted generators, and show how to turn any
iterative generator (even a bad one designed or seeded by an adversary) into a
counter assisted generator with a provably high diversity, without reducing the
quality of generators which are already cryptographically strong.Comment: Small update
Recommended from our members
Pseudorandom number generation with self programmable cellular automata
In this paper, we propose a new class of cellular automata – self programming cellular automata (SPCA) with specific application to pseudorandom number generation. By changing a cell's state transition rules in relation to factors such as its neighboring cell's states, behavioral complexity can be increased and utilized. Interplay between the state transition neighborhood and rule selection neighborhood leads to a new composite neighborhood and state transition rule that is the linear combination of two different mappings with different temporal dependencies. It is proved that when the transitional matrices for both the state transition and rule selection neighborhood are non-singular, SPCA will not exhibit non-group behavior. Good performance can be obtained using simple neighborhoods with certain CA length, transition rules etc. Certain configurations of SPCA pass all DIEHARD and ENT tests with an implementation cost lower than current reported work. Output sampling methods are also suggested to improve output efficiency by sampling the outputs of the new rule selection neighborhoods
Algorithmic Information Theory and Foundations of Probability
The use of algorithmic information theory (Kolmogorov complexity theory) to
explain the relation between mathematical probability theory and `real world'
is discussed
- …