Differentially private methods for compositional data

Confidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides a framework to conduct statistical analyses while mitigating the risk of leaking private information. Compositional data...

Descripción completa

Detalles Bibliográficos
Autores: Guo, Qi, Barrientos, Andrés F., Peña Pizarro, Víctor|||0000-0002-3801-5203
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/428017
Acceso en línea:https://hdl.handle.net/2117/428017
https://dx.doi.org/10.1080/10618600.2024.2412174
Access Level:acceso abierto
Palabra clave:Bayesian statistics
Bootstrap
Data privacy
Dirichlet distribution
Classificació AMS::62 Statistics
Àrees temàtiques de la UPC::Matemàtiques i estadística
id ES_a8dbfbc658c2d4aa92f9cf21d6f67f89
oai_identifier_str oai:upcommons.upc.edu:2117/428017
network_acronym_str ES
network_name_str España
repository_id_str
spelling Differentially private methods for compositional dataGuo, QiBarrientos, Andrés F.Peña Pizarro, Víctor|||0000-0002-3801-5203Bayesian statisticsBootstrapData privacyDirichlet distributionClassificació AMS::62 StatisticsÀrees temàtiques de la UPC::Matemàtiques i estadísticaConfidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides a framework to conduct statistical analyses while mitigating the risk of leaking private information. Compositional data, which consist of vectors with positive components that add up to a constant, have received little attention in the differential privacy literature. This article proposes differentially private approaches for analyzing compositional data based on the Dirichlet distribution. We explore several methods, including Bayesian and bootstrap procedures. For the Bayesian methods, we consider posterior inference techniques based on Markov chain Monte Carlo, Approximate Bayesian Computation, and asymptotic approximations. We conduct an extensive simulation study to compare these approaches and make evidence-based recommendations. Finally, we apply the methodology to a dataset from the American Time Use Survey.Peer Reviewed20242024-11-2520252025-04-15journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/428017https://dx.doi.org/10.1080/10618600.2024.2412174reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4280172026-05-27T15:37:01Z
dc.title.none.fl_str_mv Differentially private methods for compositional data
title Differentially private methods for compositional data
spellingShingle Differentially private methods for compositional data
Guo, Qi
Bayesian statistics
Bootstrap
Data privacy
Dirichlet distribution
Classificació AMS::62 Statistics
Àrees temàtiques de la UPC::Matemàtiques i estadística
title_short Differentially private methods for compositional data
title_full Differentially private methods for compositional data
title_fullStr Differentially private methods for compositional data
title_full_unstemmed Differentially private methods for compositional data
title_sort Differentially private methods for compositional data
dc.creator.none.fl_str_mv Guo, Qi
Barrientos, Andrés F.
Peña Pizarro, Víctor|||0000-0002-3801-5203
author Guo, Qi
author_facet Guo, Qi
Barrientos, Andrés F.
Peña Pizarro, Víctor|||0000-0002-3801-5203
author_role author
author2 Barrientos, Andrés F.
Peña Pizarro, Víctor|||0000-0002-3801-5203
author2_role author
author
dc.subject.none.fl_str_mv Bayesian statistics
Bootstrap
Data privacy
Dirichlet distribution
Classificació AMS::62 Statistics
Àrees temàtiques de la UPC::Matemàtiques i estadística
topic Bayesian statistics
Bootstrap
Data privacy
Dirichlet distribution
Classificació AMS::62 Statistics
Àrees temàtiques de la UPC::Matemàtiques i estadística
description Confidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides a framework to conduct statistical analyses while mitigating the risk of leaking private information. Compositional data, which consist of vectors with positive components that add up to a constant, have received little attention in the differential privacy literature. This article proposes differentially private approaches for analyzing compositional data based on the Dirichlet distribution. We explore several methods, including Bayesian and bootstrap procedures. For the Bayesian methods, we consider posterior inference techniques based on Markov chain Monte Carlo, Approximate Bayesian Computation, and asymptotic approximations. We conduct an extensive simulation study to compare these approaches and make evidence-based recommendations. Finally, we apply the methodology to a dataset from the American Time Use Survey.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-25
2025
2025-04-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/428017
https://dx.doi.org/10.1080/10618600.2024.2412174
url https://hdl.handle.net/2117/428017
https://dx.doi.org/10.1080/10618600.2024.2412174
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
_version_ 1869415934899257344
score 15,811543

Similares en LA Referencia