Abstract
Random numbers generated by pseudo-random and true random number generators (TRNG) are used in a wide variety of important applications. A TRNG relies on a non-deterministic source to sample random numbers. In this paper, we improve the post-processing stage of TRNGs using a heuristic evolutionary algorithm. Our post-processing algorithm decomposes the problem of improving the quality of random numbers into two phases: (i) Exact Histogram Equalization: it modifies the random numbers distribution with a specified output distribution; (ii) Stationarity Enforcement: using genetic algorithms, the output of (ii) is permuted until the random numbers meet wide-sense stationarity. We ensure that the quality of the numbers generated from the genetic algorithm is within a specified level of error defined by the user. We parallelize the genetic algorithm for improved performance. The post-processing is based on the power spectral density of the generated numbers used as a metric. We propose guideline parameters for the evolutionary algorithm to ensure fast convergence, within the first 100 generations, with a standard deviation over the specified quality level of less than 0.45. We also include a TestU01 evaluation over the random numbers generated.
Highlights
As we move towards the Big data and the exascale era, with the Internet and many communication devicesHow to cite this paper: Chan, J.J.M., Thulasiraman, P., Thomas, G. and Thulasiram, R. (2016) Ensuring Quality of Random Numbers from true random number generators (TRNG): Design and Evaluation of Post-Processing Using Genetic Algorithm
Shuffling random numbers with GA: In this stage, xm is passed through a genetic algorithm (GA) to alter its sequence order, which in consequence will have a major impact on the power spectral density
The use of a post-procesing stage for TRNGs using GA inteded for seed generation proves to satisfy the specified quality level within a desirable number of generations
Summary
As we move towards the Big data and the exascale era, with the Internet and many communication devices. (2016) Ensuring Quality of Random Numbers from TRNG: Design and Evaluation of Post-Processing Using Genetic Algorithm. Intel re-engineered [20] its own true random number generator in 2011, providing a solution to security threats on the Ivy Bridge processor In this new design, the noise circuit relied on the thermal noise and the meta-stable state of a R/S Latch, where the source of random binary digits is located at the output of the R/S Latch. The digitized noise signal generated from non-deterministic sources passes through a post-processing algorithm, to ensure the random numbers follow an uniform distribution. The post-processing algorithm proposed on [22] for TRNGs is further explored focusing on a lighter version optimized for quality and performance, ensuring the generation of random numbers meet wide sense stationarity (w.s.s.).
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