Automated evaluation of approximate matching algorithms on real data

Details

Serval ID
serval:BIB_200E1BD52A3B
Type
Article: article from journal or magazin.
Collection
Publications
Title
Automated evaluation of approximate matching algorithms on real data
Journal
Digital Investigation
Author(s)
Breitinger Frank, Roussev Vassil
ISSN
1742-2876
Publication state
Published
Issued date
05/2014
Volume
11
Number
0
Pages
S10-S17
Language
english
Abstract
Abstract Bytewise approximate matching is a relatively new area within digital forensics, but its importance is growing quickly as practitioners are looking for fast methods to screen and analyze the increasing amounts of data in forensic investigations. The essential idea is to complement the use of cryptographic hash functions to detect data objects with bytewise identical representation with the capability to find objects with bytewise similar representations. Unlike cryptographic hash functions, which have been studied and tested for a long time, approximate matching ones are still in their early development stages and evaluation methodology is still evolving. Broadly, prior approaches have used either a human in the loop to manually evaluate the goodness of similarity matches on real world data, or controlled (pseudo-random) data to perform automated evaluation. This work’s contribution is to introduce automated approximate matching evaluation on real data by relating approximate matching results to the longest common substring (LCS). Specifically, we introduce a computationally efficient {LCS} approximation and use it to obtain ground truth on the t5 set. Using the results, we evaluate three existing approximate matching schemes relative to {LCS} and analyze their performance.
Keywords
Law, Medical Laboratory Technology, Computer Science Applications
Web of science
Open Access
Yes
Create date
06/05/2021 11:01
Last modification date
06/05/2021 11:31
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