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hash HAS-160: 160 bits hash HAVAL: 128 to 256 bits hash JH: 224 to 512 bits hash LSH [19] 256 to 512 bits wide-pipe Merkle–Damgård construction: MD2: 128 bits hash MD4: 128 bits hash MD5: 128 bits Merkle–Damgård construction: MD6: up to 512 bits Merkle tree NLFSR (it is also a keyed hash function) RadioGatún: arbitrary ideal mangling ...
crypt is a POSIX C library function. It is typically used to compute the hash of user account passwords. The function outputs a text string which also encodes the salt (usually the first two characters are the salt itself and the rest is the hashed result), and identifies the hash algorithm used (defaulting to the "traditional" one explained below).
The algorithm can be described by the following pseudocode, which computes the hash of message C using the permutation table T: algorithm pearson hashing is h := 0 for each c in C loop h := T[ h xor c ] end loop return h The hash variable (h) may be initialized differently, e.g. to the length of the data (C) modulo 256.
Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem.In the dictionary problem, a data structure should maintain a collection of key–value pairs subject to operations that insert or delete pairs from the collection or that search for the value associated with a given key.
The weakness of this procedure is that information may cluster in the upper or lower bits of the bytes; this clustering will remain in the hashed result and cause more collisions than a proper randomizing hash. ASCII byte codes, for example, have an upper bit of 0, and printable strings do not use the last byte code or most of the first 32 byte ...
In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [3] [4] [5] Hashing is an example of a space-time tradeoff.
In computer science, dynamic perfect hashing is a programming technique for resolving collisions in a hash table data structure. [1] [2] [3] While more memory-intensive than its hash table counterparts, [citation needed] this technique is useful for situations where fast queries, insertions, and deletions must be made on a large set of elements.
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.