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A USB-pluggable hardware true random number generator. In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), [1] or physical random number generator [2] [3] is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a ...
Dice are an example of a mechanical hardware random number generator. When a cubical die is rolled, a random number from 1 to 6 is obtained. Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols is generated that cannot be reasonably predicted better than by random chance.
A few cryptographically secure pseudorandom number generators do not rely on cipher algorithms but try to link mathematically the difficulty of distinguishing their output from a `true' random stream to a computationally difficult problem. These approaches are theoretically important but are too slow to be practical in most applications.
The random number generator is compliant with security and cryptographic standards such as NIST SP 800-90A, [6] FIPS 140-2, and ANSI X9.82. [1] Intel also requested Cryptography Research Inc. to review the random number generator in 2012, which resulted in the paper Analysis of Intel's Ivy Bridge Digital Random Number Generator .
It was covered under the now-expired U.S. patent 5,732,138, titled "Method for seeding a pseudo-random number generator with a cryptographic hash of a digitization of a chaotic system." by Landon Curt Noll, Robert G. Mende, and Sanjeev Sisodiya. From 1997 to 2001, [2] there was a website at lavarand.sgi.com demonstrating the technique.
Non-physical true random number generator (NPTRNG), [1] also known as non-physical nondeterministic random bit generator is a true random number generator that does not have access to dedicated hardware entropy source. [2]
If one has a pseudo-random number generator whose output is "sufficiently difficult" to predict, one can generate true random numbers to use as the initial value (i.e., the seed), and then use the pseudo-random number generator to produce numbers for use in cryptographic applications.
Random number generation in kernel space was implemented for the first time for Linux [2] in 1994 by Theodore Ts'o. [6] The implementation used secure hashes rather than ciphers, [clarification needed] to avoid cryptography export restrictions that were in place when the generator was originally designed.