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→Locality-preserving hashing: {{cn}} |
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This is in contrast to [[cryptography|cryptographic]] hash functions and [[checksum]]s, which are designed to have [[Avalanche effect|random output difference between adjacent inputs]].
The first family of locality-preserving hash functions was devised as a way to facilitate [[Pipeline (computing)|data pipelining]] in implementations of [[Parallel RAM|parallel random-access machine (PRAM)]] algorithms that use [[universal hashing]] to reduce memory [[Resource_contention|contention]] and [[network congestion]].<ref name=Chin1991>{{cite thesis |last=Chin |first=Andrew |date=1991 |title=Complexity Issues in General Purpose Parallel Computing |pages=
Locality preserving hashes are related to [[space-filling curve]]s.{{how|date=March 2020}}
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| journal = 4th Cybercrime and Trustworthy Computing Workshop
| year = 2013
| pages = 7–13 | url = https://www.academia.edu/7833902
| doi = 10.1109/CTC.2013.9
| isbn = 978-1-4799-3076-0
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Testing performed in the paper on a range of file types identified the Nilsimsa hash as having a significantly higher false positive rate when compared to other similarity digest schemes such as TLSH, Ssdeep and Sdhash.
An implementation of TLSH is available as [[open-source software]].<ref>{{cite web|url=https://github.com/trendmicro/tlsh |title=TLSH |website=[[GitHub]] |access-date=2014-04-10}}</ref>
===Random projection===
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===Semantic hashing===
Semantic hashing is a technique that attempts to map input items to addresses such that closer inputs have higher [[semantic similarity]].<ref>{{Cite journal|
==LSH algorithm for nearest neighbor search==
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