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740 × 536 (15 KB) Lichtspiel: Resize, use red colour for changes, use font 'Courier 10 Pitch' 04:57, 27 November 2008: 806 × 600 (13 KB) Jorge Stolfi {{Information |Description={{en|1=Shows a typical cryptographic hash function (SHA-1) at work. Note that small differences in the input result in very different digests.
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.
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 cryptographic hash function must be able to withstand all known types of cryptanalytic attack. In theoretical cryptography, the security level of a cryptographic hash function has been defined using the following properties: Pre-image resistance Given a hash value h, it should be difficult to find any message m such that h = hash(m).
520 × 352 (97 KB) Citypeek: Valid SVG now. 01:42, 2 December 2005: 520 × 352 (18 KB) Helix84 == Summary == A typical hash function at work. Note that the hash sums always are the same size, no matter how short or long the input is and also note that the hash sums look very different even when there are only slight differences in the input.
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.
Perceptual hashing is the use of a fingerprinting algorithm that produces a snippet, hash, or fingerprint of various forms of multimedia. [ 1 ] [ 2 ] A perceptual hash is a type of locality-sensitive hash , which is analogous if features of the multimedia are similar.
UUHash is a hash algorithm employed by clients on the FastTrack network. It is employed for its ability to hash very large files in a very short period of time, even on older computers. It is employed for its ability to hash very large files in a very short period of time, even on older computers.