A posteriori disclosure risk measure for tabular data based on conditional entropy

Statistical database protection, also known as Statistical Disclosure Control (SDC), is a part of information security which tries to prevent published statistical information (tables, individual records)from disclosing the contribution of specific respondents. This paper deals with the assessment o...

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Detalhes bibliográficos
Autores: Oganian, Anna, Domingo Ferrer, Josep
Tipo de documento: artigo
Data de publicação:2003
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2099/3736
Acesso em linha:https://hdl.handle.net/2099/3736
Access Level:Acceso aberto
Palavra-chave:Statistics
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62P Applications
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spelling A posteriori disclosure risk measure for tabular data based on conditional entropyOganian, AnnaDomingo Ferrer, JosepStatisticsAplicacions (Matemàtica)Classificació AMS::62 Statistics::62P ApplicationsStatistical database protection, also known as Statistical Disclosure Control (SDC), is a part of information security which tries to prevent published statistical information (tables, individual records)from disclosing the contribution of specific respondents. This paper deals with the assessment of the disclosure risk associated to the release of tabular data. So-called sensitivity rules are currently being used to measure the disclosure risk for tables. These rules operate on an a priori basis: the data are examined and the rules are used to decide whether the data can be released as they stand or should rather be protected. In this paper, we propose to complement a priori risk assessment with a posteriori risk assessment in order to achieve a higher level of security, that is, we propose to take the protected information into account when measuring the disclosure risk. The proposed a posteriori disclosure risk measure is compatible with a broad class of disclosure protection methods and can be extended for computing disclosure risk for a set of linked tables. In the case of linked table protection via cell suppression, the proposed measure allows detection of secondary suppression patterns which offer more protection than others.Peer ReviewedInstitut d'Estadística de Catalunya20032003-01-0120072007-11-12journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3736reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 2.5 Spainhttp://creativecommons.org/licenses/by-nc-nd/2.5/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/37362026-05-27T15:37:01Z
dc.title.none.fl_str_mv A posteriori disclosure risk measure for tabular data based on conditional entropy
title A posteriori disclosure risk measure for tabular data based on conditional entropy
spellingShingle A posteriori disclosure risk measure for tabular data based on conditional entropy
Oganian, Anna
Statistics
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62P Applications
title_short A posteriori disclosure risk measure for tabular data based on conditional entropy
title_full A posteriori disclosure risk measure for tabular data based on conditional entropy
title_fullStr A posteriori disclosure risk measure for tabular data based on conditional entropy
title_full_unstemmed A posteriori disclosure risk measure for tabular data based on conditional entropy
title_sort A posteriori disclosure risk measure for tabular data based on conditional entropy
dc.creator.none.fl_str_mv Oganian, Anna
Domingo Ferrer, Josep
author Oganian, Anna
author_facet Oganian, Anna
Domingo Ferrer, Josep
author_role author
author2 Domingo Ferrer, Josep
author2_role author
dc.subject.none.fl_str_mv Statistics
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62P Applications
topic Statistics
Aplicacions (Matemàtica)
Classificació AMS::62 Statistics::62P Applications
description Statistical database protection, also known as Statistical Disclosure Control (SDC), is a part of information security which tries to prevent published statistical information (tables, individual records)from disclosing the contribution of specific respondents. This paper deals with the assessment of the disclosure risk associated to the release of tabular data. So-called sensitivity rules are currently being used to measure the disclosure risk for tables. These rules operate on an a priori basis: the data are examined and the rules are used to decide whether the data can be released as they stand or should rather be protected. In this paper, we propose to complement a priori risk assessment with a posteriori risk assessment in order to achieve a higher level of security, that is, we propose to take the protected information into account when measuring the disclosure risk. The proposed a posteriori disclosure risk measure is compatible with a broad class of disclosure protection methods and can be extended for computing disclosure risk for a set of linked tables. In the case of linked table protection via cell suppression, the proposed measure allows detection of secondary suppression patterns which offer more protection than others.
publishDate 2003
dc.date.none.fl_str_mv 2003
2003-01-01
2007
2007-11-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/3736
url https://hdl.handle.net/2099/3736
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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