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John Smith and Sandra Dee share the same hash value of 02, causing a hash collision. In computer science, a hash collision or hash clash [1] is when two distinct pieces of data in a hash table share the same hash value. The hash value in this case is derived from a hash function which takes a data input and returns a fixed length of bits. [2]
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.
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 ...
A hash of n bits can be broken in 2 n/2 time steps (evaluations of the hash function). Mathematically stated, a collision attack finds two different messages m1 and m2, such that hash(m1) = hash(m2). In a classical collision attack, the attacker has no control over the content of either message, but they are arbitrarily chosen by the algorithm.
For any fixed set of keys, using a universal family guarantees the following properties.. For any fixed in , the expected number of keys in the bin () is /.When implementing hash tables by chaining, this number is proportional to the expected running time of an operation involving the key (for example a query, insertion or deletion).
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.