Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis

k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation...

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Detalles Bibliográficos
Autores: Monedero, DR, Mezher, AM, Colome, XC, Forne, J, Soriano, M
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p1434
Acceso en línea:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1434
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068614484&doi=10.1016%2fj.ins.2019.07.042&partnerID=40&md5=6e5191eb8ab29c7516fbf387f649f99d
Access Level:acceso abierto
Palabra clave:Algebra
Data privacy
Large dataset
Numerical methods
Computational demands
Data utilities
Information utility
K-Anonymity
Large-scale datasets
Microaggregation
Statistical disclosure Control
Statistical techniques
Principal component analysis
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spelling Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysisMonedero, DRMezher, AMColome, XCForne, JSoriano, MAlgebraData privacyLarge datasetNumerical methodsComputational demandsData utilitiesInformation utilityK-AnonymityLarge-scale datasetsMicroaggregationStatistical disclosure ControlStatistical techniquesPrincipal component analysisk-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effective reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes. © 2019 The Authors. Preprint submitted to Elsevier, Inc. © 2019 Elsevier Inc.Elsevier Inc.2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1434https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068614484&doi=10.1016%2fj.ins.2019.07.042&partnerID=40&md5=6e5191eb8ab29c7516fbf387f649f99dINFORMATION SCIENCESISSN: 00200255ISSNe: 18726291reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p14342026-06-17T11:44:47Z
dc.title.none.fl_str_mv Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
title Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
spellingShingle Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
Monedero, DR
Algebra
Data privacy
Large dataset
Numerical methods
Computational demands
Data utilities
Information utility
K-Anonymity
Large-scale datasets
Microaggregation
Statistical disclosure Control
Statistical techniques
Principal component analysis
title_short Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
title_full Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
title_fullStr Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
title_full_unstemmed Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
title_sort Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
dc.creator.none.fl_str_mv Monedero, DR
Mezher, AM
Colome, XC
Forne, J
Soriano, M
author Monedero, DR
author_facet Monedero, DR
Mezher, AM
Colome, XC
Forne, J
Soriano, M
author_role author
author2 Mezher, AM
Colome, XC
Forne, J
Soriano, M
author2_role author
author
author
author
dc.subject.none.fl_str_mv Algebra
Data privacy
Large dataset
Numerical methods
Computational demands
Data utilities
Information utility
K-Anonymity
Large-scale datasets
Microaggregation
Statistical disclosure Control
Statistical techniques
Principal component analysis
topic Algebra
Data privacy
Large dataset
Numerical methods
Computational demands
Data utilities
Information utility
K-Anonymity
Large-scale datasets
Microaggregation
Statistical disclosure Control
Statistical techniques
Principal component analysis
description k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effective reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes. © 2019 The Authors. Preprint submitted to Elsevier, Inc. © 2019 Elsevier Inc.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1434
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068614484&doi=10.1016%2fj.ins.2019.07.042&partnerID=40&md5=6e5191eb8ab29c7516fbf387f649f99d
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1434
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068614484&doi=10.1016%2fj.ins.2019.07.042&partnerID=40&md5=6e5191eb8ab29c7516fbf387f649f99d
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier Inc.
publisher.none.fl_str_mv Elsevier Inc.
dc.source.none.fl_str_mv INFORMATION SCIENCES
ISSN: 00200255
ISSNe: 18726291
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
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