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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; Ironclad a Common Lisp cryptography package containing a Whirlpool implementation; The ISO/IEC 10118 ...
Hopscotch hashing. Here, H is 4. Gray entries are occupied. In part (a), the item x is added with a hash value of 6. A linear probe finds that entry 13 is empty. Because 13 is more than 4 entries away from 6, the algorithm looks for an earlier entry to swap with 13. The first place to look in is H−1 = 3 entries before, at entry 10.
The FNV-1 hash algorithm is as follows: [9] [10] algorithm fnv-1 is hash := FNV_offset_basis for each byte_of_data to be hashed do hash := hash × FNV_prime hash := hash XOR byte_of_data return hash. In the above pseudocode, all variables are unsigned integers. All variables, except for byte_of_data, have the same number of bits as the FNV hash.
A universal hashing scheme is a randomized algorithm that selects a hash function h among a family of such functions, in such a way that the probability of a collision of any two distinct keys is 1/m, where m is the number of distinct hash values desired—independently of the two keys. Universal hashing ensures (in a probabilistic sense) that ...
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 ...
SHA-2 (Secure Hash Algorithm 2) is a set of cryptographic hash functions designed by the United States National Security Agency (NSA) and first published in 2001. [3] [4] They are built using the Merkle–Damgård construction, from a one-way compression function itself built using the Davies–Meyer structure from a specialized block cipher.
The salt and hash are then stored in the database. To later test if a password a user enters is correct, the same process can be performed on it (appending that user's salt to the password and calculating the resultant hash): if the result does not match the stored hash, it could not have been the correct password that was entered.
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.