article

A recursive search algorithm for statistical disclosure assessment

Authors:

Anna M. Manning,

David J. Haglin,

John A. KeaneAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 16, Issue 2

Pages 165 - 196

https://doi.org/10.1007/s10618-007-0078-6

Published: 01 April 2008 Publication History

Abstract

A new algorithm, SUDA2, is presented which finds minimally unique itemsets i.e., minimal itemsets of frequency one. These itemsets, referred to as Minimal Sample Uniques (MSUs), are important for statistical agencies who wish to estimate the risk of disclosure of their datasets. SUDA2 is a recursive algorithm which uses new observations about the properties of MSUs to prune and traverse the search space. Experimental comparisons with previous work demonstrate that SUDA2 is several orders of magnitude faster, enabling datasets of significantly more columns to be addressed. The ability of SUDA2 to identify the boundaries of the search space for MSUs is clearly demonstrated.

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Carvalho TMoniz NFaria PAntunes L(2023)Survey on Privacy-Preserving Techniques for Microdata PublicationACM Computing Surveys10.1145/358876555:14s(1-42)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3588765
Orooji MRabbanian SKnapp G(2023)Flexible adversary disclosure risk measure for identity and attribute disclosure attacksInternational Journal of Information Security10.1007/s10207-022-00654-y22:3(631-645)Online publication date: 5-Jan-2023
https://dl.acm.org/doi/10.1007/s10207-022-00654-y
Templ MSariyar M(2022)A systematic overview on methods to protect sensitive data provided for various analysesInternational Journal of Information Security10.1007/s10207-022-00607-521:6(1233-1246)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10207-022-00607-5
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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 16, Issue 2

April 2008

114 pages

ISSN:1384-5810

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Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2008

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Carvalho TMoniz NFaria PAntunes L(2023)Survey on Privacy-Preserving Techniques for Microdata PublicationACM Computing Surveys10.1145/358876555:14s(1-42)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3588765
Orooji MRabbanian SKnapp G(2023)Flexible adversary disclosure risk measure for identity and attribute disclosure attacksInternational Journal of Information Security10.1007/s10207-022-00654-y22:3(631-645)Online publication date: 5-Jan-2023
https://dl.acm.org/doi/10.1007/s10207-022-00654-y
Templ MSariyar M(2022)A systematic overview on methods to protect sensitive data provided for various analysesInternational Journal of Information Security10.1007/s10207-022-00607-521:6(1233-1246)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10207-022-00607-5
Bouasker SHamrouni TYahia S(2015)Efficient mining of new concise representations of rare correlated patterns\m{1}Intelligent Data Analysis10.5555/2768391.276840019:2(359-390)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.5555/2768391.2768400
Hamrouni T(2012)Key roles of closed sets and minimal generators in concise representations of frequent patternsIntelligent Data Analysis10.5555/2595513.259551716:4(581-631)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.5555/2595513.2595517
Kowarik ATempl MMeindl BFonteneau FPrantner B(2012)Testing of IHSN c++ code and inclusion of new methods into sdcmicroProceedings of the 2012 international conference on Privacy in Statistical Databases10.1007/978-3-642-33627-0_6(63-77)Online publication date: 26-Sep-2012
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Gupta AMittal ABhattacharya ASadaphal VHaritsa JDayal UDeshpande P(2011)Minimally infrequent itemset mining using pattern-growth paradigm and residual treesProceedings of the 17th International Conference on Management of Data10.5555/2591338.2591351(1-12)Online publication date: 19-Dec-2011
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A New Algorithm for Finding Minimal Sample Uniques for Use in Statistical Disclosure Assessment

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