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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 .
In cryptography, the Merkle–Damgård construction or Merkle–Damgård hash function is a method of building collision-resistant cryptographic hash functions from collision-resistant one-way compression functions. [1]: 145 This construction was used in the design of many popular hash algorithms such as MD5, SHA-1, and SHA-2.
Hashing is used in database systems as a method to protect sensitive data such as passwords; however it is also used to improve the efficiency of database referencing. [26] Inputted data is manipulated by a hashing algorithm. The hashing algorithm converts the inputted data into a string of fixed length that can then be stored in a database.
Through this method, a trusted source can calculate the hash of an original data file and subscribers can verify the integrity of the data. The subscriber simply compares a hash of the received data file with the known hash from the trusted source. This can lead to two situations: the hash being the same or the hash being different. If the hash ...
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.
The resulting value was reduced by modulo, folding, or some other operation to produce a hash table index. The original Zobrist hash was stored in the table as the representation of the position. Later, the method was extended to hashing integers by representing each byte in each of 4 possible positions in the word by a unique 32-bit random number.
In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped because the mapping between the keys and the slots is defined by a modular operation. Consistent hashing evenly distributes cache keys across shards, even if some of the shards crash or become unavailable. [3]
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.