Pseudorandom Number Generation in Smart Cards: An Implementation, Performance and Randomness Analysis

Smart cards rely on pseudorandom number generators to provide uniqueness and freshness in their cryptographic services i.e. encryption and digital signatures. Their implementations are kept proprietary by smart card manufacturers in order to remain competitive. In this paper we look at how these generators are implemented in general purpose computers. How architecture of such generators can be modified to suit the smart card environment. Six variations of this modified model were implemented in Java Card along with the analysis of their performance and randomness. To analyse the randomness of the implemented algorithms, the NIST statistical test suite is used. Finally, an overall analysis is provided, that is useful for smart card designers to make informed decisions when implementing pseudorandom number generators

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

Spectral analysis of random number generators

This paper is based on the theory developed by Dr. Evangelos Yfantis, professor of Computer Science at University of Nevada, Las Vegas. In this paper, we describe a method for testing the fairness of pseudorandom number generators using the Discrete Fourier Transform. We will show how the concept of a random process can be used in a representation for random discrete time signals. Using this concept, we have focused on the mathematical representations of the spectral analysis of a fair pseudorandom number generator. From this representation, a reasonable spectral expectation is determined. An algorithm which applies the developed method is described, and a modified shift register random number generator is used to produce sample data

Pseudorandom Number Generators and the Square Site Percolation Threshold

A select collection of pseudorandom number generators is applied to a Monte Carlo study of the two dimensional square site percolation model. A generator suitable for high precision calculations is identified from an application specific test of randomness. After extended computation and analysis, an ostensibly reliable value of pc = 0.59274598(4) is obtained for the percolation threshold.Comment: 11 pages, 6 figure

Hurst's Rescaled Range Statistical Analysis for Pseudorandom Number Generators used in Physical Simulations

The rescaled range statistical analysis (R/S) is proposed as a new method to detect correlations in pseudorandom number generators used in Monte Carlo simulations. In an extensive test it is demonstrated that the RS analysis provides a very sensitive method to reveal hidden long run and short run correlations. Several widely used and also some recently proposed pseudorandom number generators are subjected to this test. In many generators correlations are detected and quantified.Comment: 12 pages, 12 figures, 6 tables. Replaces previous version to correct citation [19