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Nilsimsa Hash is an anti-spam focused locality-sensitive hashing algorithm. ssdeep is a fuzzy hashing tool based on context-piecewise triggered hashing to compare files. [4] sdhash is a fuzzy hashing tool based on using bloom filters to determine whether one file is contained within another or how similar two files are to each other. [11]
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.
Fuzzy extractors are a method that allows biometric data to be used as inputs to standard cryptographic techniques, to enhance computer security. "Fuzzy", in this context, refers to the fact that the fixed values required for cryptography will be extracted from values close to but not identical to the original key, without compromising the security required.
Nilsimsa is an anti-spam focused locality-sensitive hashing algorithm originally proposed the cmeclax remailer operator in 2001 [1] and then reviewed by Ernesto Damiani et al. in their 2004 paper titled, "An Open Digest-based Technique for Spam Detection". [2]
Fuzzy logic is an important concept in medical decision making. Since medical and healthcare data can be subjective or fuzzy, applications in this domain have a great potential to benefit a lot by using fuzzy-logic-based approaches. Fuzzy logic can be used in many different aspects within the medical decision making framework.
Fuzzy hashing, also known as similarity hashing, [17] is a technique for detecting data that is similar, but not exactly the same, as other data. This is in contrast to cryptographic hash functions, which are designed to have significantly different hashes for even minor differences.
This is especially true of cryptographic hash functions, which may be used to detect many data corruption errors and verify overall data integrity; if the computed checksum for the current data input matches the stored value of a previously computed checksum, there is a very high probability the data has not been accidentally altered or corrupted.
A fuzzy Mediawiki search for "angry emoticon" has as a suggested result "andré emotions" In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly).