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  2. Perfect hash function - Wikipedia

    en.wikipedia.org/wiki/Perfect_hash_function

    A perfect hash function for the four names shown A minimal perfect hash function for the four names shown. In computer science, a perfect hash function h for a set S is a hash function that maps distinct elements in S to a set of m integers, with no collisions. In mathematical terms, it is an injective function.

  3. MurmurHash - Wikipedia

    en.wikipedia.org/wiki/MurmurHash

    MurmurHash is a non-cryptographic hash function suitable for general hash-based lookup. [1] [2] [3] It was created by Austin Appleby in 2008 [4] and, as of 8 January 2016, [5] is hosted on GitHub along with its test suite named SMHasher.

  4. md5sum - Wikipedia

    en.wikipedia.org/wiki/Md5sum

    The MD5 hash functions as a compact digital fingerprint of a file. As with all such hashing algorithms, there is theoretically an unlimited number of files that will have any given MD5 hash. However, it is very unlikely that any two non-identical files in the real world will have the same MD5 hash, unless they have been specifically created to ...

  5. Jenkins hash function - Wikipedia

    en.wikipedia.org/wiki/Jenkins_hash_function

    The lookup3 function consumes input in 12 byte (96 bit) chunks. [9] It may be appropriate when speed is more important than simplicity. Note, though, that any speed improvement from the use of this hash is only likely to be useful for large keys, and that the increased complexity may also have speed consequences such as preventing an optimizing compiler from inlining the hash function.

  6. Whirlpool (hash function) - Wikipedia

    en.wikipedia.org/wiki/Whirlpool_(hash_function)

    whirlpool on GitHub – An open source Go implementation of the latest revision of Whirlpool; A Matlab Implementation of the Whirlpool Hashing Function; RHash, an open source command-line tool, which can calculate and verify Whirlpool hash. Perl Whirlpool module at CPAN; Digest module implementing the Whirlpool hashing algorithm in Ruby

  7. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.

  8. Cryptographically secure pseudorandom number generator

    en.wikipedia.org/wiki/Cryptographically_secure...

    In the asymptotic setting, a family of deterministic polynomial time computable functions : {,} {,} for some polynomial p, is a pseudorandom number generator (PRNG, or PRG in some references), if it stretches the length of its input (() > for any k), and if its output is computationally indistinguishable from true randomness, i.e. for any probabilistic polynomial time algorithm A, which ...

  9. SipHash - Wikipedia

    en.wikipedia.org/wiki/SipHash

    SipHash computes a 64-bit message authentication code from a variable-length message and 128-bit secret key. It was designed to be efficient even for short inputs, with performance comparable to non-cryptographic hash functions, such as CityHash; [4]: 496 [2] this can be used to prevent denial-of-service attacks against hash tables ("hash flooding"), [5] or to authenticate network packets.