Hardware random number generator

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Hardware random number generator

In <a href="/en/Computing" title="Computing" class="wl">computing</a>, a hardware random number generator (HRNG) or true random number generator (TRNG) is a device that <a href="/en/Random_number_generation" title="Random number generation" class="wl">generates random numbers</a> from a <a href="/en/Physical_process" title="Physical process" class="mw-redirect wl">physical process</a>, rather than by means of an <a href="/en/Algorithm" title="Algorithm" class="wl">algorithm</a>. Such devices are often based on microscopic phenomena that generate low-level, <a href="/en/Statistically_random" title="Statistically random" class="mw-redirect wl">statistically random</a> "<a href="/en/Noise_(signal_processing)" title="Noise (signal processing)" class="wl">noise</a>" signals, such as <a href="/en/Thermal_noise" title="Thermal noise" class="mw-redirect wl">thermal noise</a>, the <a href="/en/Photoelectric_effect" title="Photoelectric effect" class="wl">photoelectric effect</a>, involving a <a href="/en/Beam_splitter" title="Beam splitter" class="wl">beam splitter</a>, and other <a href="/en/Quantum" title="Quantum" class="wl">quantum</a> phenomena. These <a href="/en/Stochastic" title="Stochastic" class="wl">stochastic</a> processes are, in theory, completely unpredictable for as long as an equation governing such phenomena is unknown or uncomputable. This is in contrast to the paradigm of <a href="/en/Pseudorandom_number_generator" title="Pseudorandom number generator" class="wl">pseudo-random number generation</a> commonly implemented in <a href="/en/Computer_program" title="Computer program" class="wl">computer programs</a>.

<div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none" about="#mwt1">Cryptographic device</div>
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<figure class="mw-default-size mw-halign-right wiki-thumb"><a href="/en/File:Sun-crypto-accelerator-1000.jpg" class="mw-file-description image media-thumb" data-hash="File:Sun-crypto-accelerator-1000.jpg"><img loading="lazy" src="//upload.wikimedia.org/wikipedia/commons/thumb/7/74/Sun-crypto-accelerator-1000.jpg/220px-Sun-crypto-accelerator-1000.jpg" decoding="async" data-file-width="2907" data-file-height="1635" data-file-type="bitmap" height="124" width="220" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/7/74/Sun-crypto-accelerator-1000.jpg/330px-Sun-crypto-accelerator-1000.jpg 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/7/74/Sun-crypto-accelerator-1000.jpg/440px-Sun-crypto-accelerator-1000.jpg 2x" alt="Sun-crypto-accelerator-1000.jpg"></a><figcaption>This <a href="/en/TLS_acceleration" title="TLS acceleration" class="wl">TLS accelerator</a> <a href="/en/Hardware_acceleration" title="Hardware acceleration" class="wl">computer card</a> uses a hardware random number generator to generate <a href="/en/Cryptographic_key" title="Cryptographic key" class="mw-redirect wl">cryptographic keys</a> to encrypt data sent over computer networks.</figcaption></figure>

A hardware random number generator typically consists of a <a href="/en/Transducer" title="Transducer" class="wl">transducer</a> to convert some aspect of the physical phenomena to an electrical signal, an <a href="/en/Amplifier" title="Amplifier" class="wl">amplifier</a> and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of <a href="/en/Analog-to-digital_converter" title="Analog-to-digital converter" class="wl">analog-to-digital converter</a> 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 obtained.

The main application for electronic hardware random number generators is in <a href="/en/Cryptography" title="Cryptography" class="wl">cryptography</a>, where they are used to generate random <a href="/en/Cryptographic_key" title="Cryptographic key" class="mw-redirect wl">cryptographic keys</a> to transmit data securely. They are widely used in Internet encryption protocols such as <a href="/en/Transport_Layer_Security" title="Transport Layer Security" class="wl">Transport Layer Security</a> (TLS).

Random number generators can also be built from "random" macroscopic processes, using devices such as <a href="/en/Coin_flipping" title="Coin flipping" class="wl">coin flipping</a>, <a href="/en/Dice" title="Dice" class="wl">dice</a>, <a href="/en/Roulette" title="Roulette" class="wl">roulette</a> wheels and <a href="/en/Lottery_machine" title="Lottery machine" class="wl">lottery machines</a>. The presence of unpredictability in these phenomena is supported by the theory of <a href="/en/Instability" title="Instability" class="wl">unstable</a> <a href="/en/Dynamical_system" title="Dynamical system" class="wl">dynamical systems</a> and <a href="/en/Chaos_theory" title="Chaos theory" class="wl">chaos theory</a>. Even though macroscopic processes are deterministic under <a href="/en/Newtonian_mechanics" title="Newtonian mechanics" class="mw-redirect wl">Newtonian mechanics</a>, the output of a well-designed device can be impractical to predict in practice, because it depends on the sensitive, micro-details of the <a href="/en/Initial_conditions" title="Initial conditions" class="mw-redirect wl">initial conditions</a> of each use.

Although dice have been mostly used in <a href="/en/Gambling" title="Gambling" class="wl">gambling</a>, and as "randomizing" elements in games (e.g. <a href="/en/Role_playing_game" title="Role playing game" class="mw-redirect wl">role playing games</a>), the <a href="/en/Victorian_era" title="Victorian era" class="wl">Victorian</a> scientist <a href="/en/Francis_Galton" title="Francis Galton" class="wl">Francis Galton</a> described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.<a href="#cite_note-1" style="counter-reset: mw-Ref 1;">[1]</a>

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 "<a href="/en/Random_seed" title="Random seed" class="wl">seed</a>" for a faster <a href="/en/Cryptographically_secure_pseudorandom_number_generator" title="Cryptographically secure pseudorandom number generator" class="wl">cryptographically secure pseudorandom number generator</a>, which then generates a <a href="/en/Pseudorandom" title="Pseudorandom" class="mw-redirect wl">pseudorandom</a> output sequence at a much higher data rate.

With random number generators based on a noisy classical system or an elementary quantum measurement, assertions of unpredictability should be based on a careful model describing the underlying physics. Yet any such model must make a number of assumptions that may not be valid, and are difficult to verify. But starting in 2010, "Einstein-certified" quantum physics experiments have been able to demonstrate, sometimes even to remote observers, that the bits they produce are unpredictable, requiring only very mild assumptions about signals not being able to travel faster than the speed of light.<a href="#cite_note-2" style="counter-reset: mw-Ref 2;">[2]</a><a href="#cite_note-3" style="counter-reset: mw-Ref 3;">[3]</a><a href="#cite_note-4" style="counter-reset: mw-Ref 4;">[4]</a><a href="#cite_note-5" style="counter-reset: mw-Ref 5;">[5]</a>

In computing, a hardware random number generator or true random number generator 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 for as long as an equation governing such phenomena is unknown or uncomputable. This is in contrast to the paradigm of pseudo-random number generation commonly implemented in computer programs. 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 obtained. 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 Transport Layer Security . 