Research Article
How Cuckoo Filter Can Improve Existing Approximate Matching Techniques
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@INPROCEEDINGS{10.1007/978-3-319-25512-5_4, author={Vikas Gupta and Frank Breitinger}, title={How Cuckoo Filter Can Improve Existing Approximate Matching Techniques}, proceedings={Digital Forensics and Cyber Crime. 7th International Conference, ICDF2C 2015, Seoul, South Korea, October 6--8, 2015, Revised Selected Papers}, proceedings_a={ICDF2C}, year={2015}, month={10}, keywords={Approximate matching Similarity hashing Bloom filter Cuckoo filter Fuzzy hashing Similarity hashing }, doi={10.1007/978-3-319-25512-5_4} }- Vikas Gupta
Frank Breitinger
Year: 2015
How Cuckoo Filter Can Improve Existing Approximate Matching Techniques
ICDF2C
Springer
DOI: 10.1007/978-3-319-25512-5_4
Abstract
In recent years, approximate matching algorithms have become an important component in digital forensic research and have been adopted in some other working areas as well. Currently there are several approaches, but and especially attract the attention of the community because of their good overall performance (runtime, compression and detection rates). Although both approaches have quite a different proceeding, their final output (the similarity digest) is very similar as both utilize Bloom filters. This data structure was presented in 1970 and thus has been used for a while. Recently, a new data structure was proposed which claimed to be faster and have a smaller memory footprint than Bloom filter -
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