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RNG stands for random number generator, a device or algorithm that produces a sequence of numbers or symbols that cannot be reasonably predicted. Learn about the methods, applications and types of RNG, such as true and pseudorandom numbers.
Cipher algorithms and cryptographic hashes can be used as very high-quality pseudorandom number generators. However, generally they are considerably slower (typically by a factor 2–10) than fast, non-cryptographic random number generators.
Mersenne Twister is a pseudorandom number generator based on a Mersenne prime. It has a long period, good equidistribution and various variants for different platforms and applications.
A hardware random number generator (HRNG) is a device that generates random numbers from a physical process capable of producing entropy. Learn about the history, uses, and types of HRNGs, and how they differ from pseudorandom number generators (PRNGs).
A random number is generated by a random process such as throwing dice. Learn about the common understanding, real world consequences, and flaws of random number generation, as well as algorithms and implementations.
With keys having low entropy (i.e., relatively easily guessable by attackers), security is likely to be compromised. To illustrate, imagine if a simple 32 bit linear congruential pseudo-random number generator of the type supplied with most programming languages (e.g., as the 'rand' or 'rnd' function) is used as a source of keys.
Learn about the history and uses of random number tables, which are generated by various methods and tested for statistical randomness. Find out how they differ from cryptographic random numbers and how they are accessed by computers.
The Lehmer random number generator [1] (named after D. H. Lehmer), sometimes also referred to as the Park–Miller random number generator (after Stephen K. Park and Keith W. Miller), is a type of linear congruential generator (LCG) that operates in multiplicative group of integers modulo n.