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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 ...
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 ...
Checksum algorithms, such as CRC32 and other cyclic redundancy checks, are designed to meet much weaker requirements and are generally unsuitable as cryptographic hash functions. For example, a CRC was used for message integrity in the WEP encryption standard, but an attack was readily discovered, which exploited the linearity of the checksum.
The following tables compare general and technical information for a number of cryptographic hash functions. See the individual functions' articles for further information. This article is not all-inclusive or necessarily up-to-date. An overview of hash function security/cryptanalysis can be found at hash function security summary.
For example, the family made of the identity function () = is clearly universal, but the family made of the function () = ′ fails to be universal. UMAC and Poly1305-AES and several other message authentication code algorithms are based on universal hashing.
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.
A hash function is k-perfect if at most k elements from S are mapped onto the same value in the range. The "hash, displace, and compress" algorithm can be used to construct k-perfect hash functions by allowing up to k collisions. The changes necessary to accomplish this are minimal, and are underlined in the adapted pseudocode below:
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.