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  2. Non-cryptographic hash function - Wikipedia

    en.wikipedia.org/wiki/Non-cryptographic_hash...

    Non-cryptographic hash functions optimized for software frequently involve the multiplication operation. Since in-hardware multiplication is resource-intensive and frequency-limiting, ASIC-friendlier designs had been proposed, including SipHash (which has an additional benefit of being able to use a secret key for message authentication), NSGAhash, and XORhash.

  3. Comparison of cryptography libraries - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_cryptography...

    Comparison of supported cryptographic hash functions. Here hash functions are defined as taking an arbitrary length message and producing a fixed size output that is virtually impossible to use for recreating the original message.

  4. Hash table - Wikipedia

    en.wikipedia.org/wiki/Hash_table

    A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value can be found. During lookup, the key is hashed and the resulting hash indicates where the corresponding value is stored. A map implemented by a hash table is called a hash map.

  5. List of hash functions - Wikipedia

    en.wikipedia.org/wiki/List_of_hash_functions

    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 ...

  6. Hashrate - Wikipedia

    en.wikipedia.org/wiki/Hashrate

    The proof-of-work distributed computing schemes, including Bitcoin, frequently use cryptographic hashes as a proof-of-work algorithm. Hashrate is a measure of the total computational power of all participating nodes expressed in units of hash calculations per second.

  7. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    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.

  8. BLAKE (hash function) - Wikipedia

    en.wikipedia.org/wiki/BLAKE_(hash_function)

    BLAKE is a cryptographic hash function based on Daniel J. Bernstein's ChaCha stream cipher, but a permuted copy of the input block, XORed with round constants, is added before each ChaCha round. Like SHA-2 , there are two variants differing in the word size.

  9. Chord (peer-to-peer) - Wikipedia

    en.wikipedia.org/wiki/Chord_(peer-to-peer)

    Nodes and keys are assigned an -bit identifier using consistent hashing.The SHA-1 algorithm is the base hashing function for consistent hashing. Consistent hashing is integral to the robustness and performance of Chord because both keys and nodes (in fact, their IP addresses) are uniformly distributed in the same identifier space with a negligible possibility of collision.