Hardware random number generator | Wikipedia audio article
Channel: Subhajit Sahu
Date: 14/01/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Hardware_random_number_generator


00:03:17 1 Uses
00:04:11 1.1 Cryptography
00:05:29 2 Early work
00:07:57 3 Physical phenomena with random properties
00:08:08 3.1 Quantum random properties
00:11:19 3.2 Classical random properties
00:13:46 3.2.1 Clock drift
00:17:44 4 Dealing with bias
00:18:40 4.1 Software whitening
00:23:19 4.2 PRNG with periodically refreshed random key
00:24:00 5 Using observed events
00:27:22 6 Problems
00:28:20 6.1 Attacks
00:28:56 6.2 Estimating entropy
00:29:35 6.3 Performance test
00:31:35 7 See also



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SUMMARY
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In computing, a hardware random number generator (HRNG) or true random number generator (TRNG) is a device that generates random numbers from a physical process, rather than by means of an algorithm.  Such devices are often based on microscopic phenomena that generate low-level, statistically random "noise" signals, such as thermal noise, the photoelectric effect, involving a beam splitter, and other quantum phenomena. These stochastic processes are, in theory, completely unpredictable, and the theory's assertions of unpredictability are subject to experimental test. This is in contrast to the common paradigm of pseudo-random number generation commonly implemented in computer programs or cryptographic hardware.
A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1. By repeatedly sampling the randomly varying signal, a series of random numbers is attained.
The main application for electronic hardware random number generators is in cryptography, where they are used to generate random cryptographic keys to transmit data securely.  They are widely used in Internet encryption protocols such as Secure Sockets Layer (SSL).
Random number generators can also be built from "random" macroscopic processes, using devices such as coin flipping, dice, roulette wheels and lottery machines. The presence of unpredictability in these phenomena can be justified by the theory of unstable dynamical systems and chaos theory. Even though macroscopic processes are deterministic under Newtonian mechanics, the output of a well-designed device like a roulette wheel cannot be predicted in practice, because it depends on the sensitive, micro-details of the initial conditions of each use.
Although dice have been mostly used in gambling, and as "randomizing" elements in games (e.g. role playing games), the Victorian scientist Francis Galton described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.Hardware random number generators generally produce only a limited number of random bits per second.  In order to increase the available output data rate, they are often used to generate the "seed" for a faster cryptographically secure pseudorandom number generator, which then generates a pseudorandom output sequence at a much higher data rate.