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  2. List of random number generators - Wikipedia

    en.wikipedia.org/wiki/List_of_random_number...

    However, generally they are considerably slower (typically by a factor 2–10) than fast, non-cryptographic random number generators. These include: Stream ciphers. Popular choices are Salsa20 or ChaCha (often with the number of rounds reduced to 8 for speed), ISAAC, HC-128 and RC4. Block ciphers in counter mode.

  3. Mersenne Twister - Wikipedia

    en.wikipedia.org/wiki/Mersenne_Twister

    CryptMT is a stream cipher and cryptographically secure pseudorandom number generator which uses Mersenne Twister internally. It was developed by Matsumoto and Nishimura alongside Mariko Hagita and Mutsuo Saito. It has been submitted to the eSTREAM project of the eCRYPT network.

  4. Random number generation - Wikipedia

    en.wikipedia.org/wiki/Random_number_generation

    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 that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain ...

  5. Hardware random number generator - Wikipedia

    en.wikipedia.org/wiki/Hardware_random_number...

    In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), or physical random number generator 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 physical entropy source ...

  6. Random seed - Wikipedia

    en.wikipedia.org/wiki/Random_seed

    A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator. For a seed to be used in a pseudorandom number generator, it does not need to be random.

  7. Applications of randomness - Wikipedia

    en.wikipedia.org/wiki/Applications_of_randomness

    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.

  8. Blum Blum Shub - Wikipedia

    en.wikipedia.org/wiki/Blum_Blum_Shub

    import sympy def blum_blum_shub (p1, p2, seed, iterations): assert p1 % 4 == 3 assert p2 % 4 == 3 assert sympy. isprime (p1 // 2) assert sympy. isprime (p2 // 2) n = p1 * p2 numbers = [] for _ in range (iterations): seed = (seed ** 2) % n if seed in numbers: print (f "The RNG has fallen into a loop at {len (numbers)} steps") return numbers ...

  9. Xorshift - Wikipedia

    en.wikipedia.org/wiki/Xorshift

    xoshiro256** is the family's general-purpose random 64-bit number generator. It is used in GNU Fortran compiler, Lua (as of Lua 5.4), and the .NET framework (as of .NET 6.0).

  10. Pseudorandom number generator - Wikipedia

    en.wikipedia.org/wiki/Pseudorandom_number_generator

    A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers.

  11. Python syntax and semantics - Wikipedia

    en.wikipedia.org/wiki/Python_syntax_and_semantics

    Numbers. Numeric literals in Python are of the normal sort, e.g. 0, -1, 3.4, 3.5e-8. Python has arbitrary-length integers and automatically increases their storage size as necessary. Prior to Python 3, there were two kinds of integral numbers: traditional fixed size integers and "long" integers of arbitrary size.