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Cells in the hash table are assigned one of three states in this method – occupied, empty, or deleted. If a hash collision occurs, the table will be probed to move the record to an alternate cell that is stated as empty. There are different types of probing that take place when a hash collision happens and this method is implemented.
The MD construction is inherently sequential. There is a parallel algorithm [13] which constructs a collision-resistant hash function from a collision-resistant compression function. The hash function PARSHA-256 [14] was designed using the parallel algorithm and the compression function of SHA-256.
Rainbow tables use a refined algorithm with a different reduction function for each "link" in a chain, so that when there is a hash collision in two or more chains, the chains will not merge as long as the collision doesn't occur at the same position in each chain.
SHA-2: A family of two similar hash functions, with different block sizes, known as SHA-256 and SHA-512. They differ in the word size; SHA-256 uses 32-bit words where SHA-512 uses 64-bit words. There are also truncated versions of each standard, known as SHA-224, SHA-384, SHA-512/224 and SHA-512/256. These were also designed by the NSA.
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.
For example, let n be significantly less than 2 b. Consider a pseudorandom number generator function P(key) that is uniform on the interval [0, 2 b − 1]. A hash function uniform on the interval [0, n − 1] is n P(key) / 2 b. We can replace the division by a (possibly faster) right bit shift: n P(key) >> b.
Feature hashing generally suffers from hash collision, which means that there exist pairs of different tokens with the same hash: ′, = (′) =. A machine learning model trained on feature-hashed words would then have difficulty distinguishing t {\displaystyle t} and t ′ {\displaystyle t'} , essentially because v {\displaystyle v} is polysemic .
MurmurHash is a non-cryptographic hash function suitable for general hash-based lookup. [1] [2] [3] It was created by Austin Appleby in 2008 [4] and, as of 8 January 2016, [5] is hosted on GitHub along with its test suite named SMHasher.