Conciliating privacy and utility in data releases via individual differential privacy and microaggregation

ε-Differential privacy (DP) is a well-known privacy model that offers strong privacy guarantees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data rel...

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Detalles Bibliográficos
Autores: Soria-Comas, Jordi, Sanchez, David, Domingo-Ferrer, Josep, Martinez, Sergio, Del Vasto-Terrientes, Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/152391
Acceso en línea:http://hdl.handle.net/10609/152391
Access Level:acceso abierto
Palabra clave:individual differential privacy
machine learning
data microaggregation
data releases
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spelling Conciliating privacy and utility in data releases via individual differential privacy and microaggregationSoria-Comas, JordiSanchez, DavidDomingo-Ferrer, JosepMartinez, SergioDel Vasto-Terrientes, Luisindividual differential privacymachine learningdata microaggregationdata releasesε-Differential privacy (DP) is a well-known privacy model that offers strong privacy guarantees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data releases have used weak privacy parameters (large ε), which dilute the privacy guarantees of DP. In this work, we tackle this issue by using an alternative formulation of the DP privacy guarantees, named ε-individual differential privacy (iDP), which causes less data distortion while providing the same protection as DP to subjects. We enforce iDP in data releases by relying on attribute masking plus a pre-processing step based on data microaggregation. The goal of this step is to reduce the sensitivity to record changes, which determines the amount of noise required to enforce iDP (and DP). Specifically, we propose data microaggregation strategies designed for iDP whose sensitivities are significantly lower than those used in DP. As a result, we obtain iDP-protected data with significantly better utility than with DP. We report on experiments that show how our approach can provide strong privacy (small ε) while yielding protected data that do not significantly degrade the accuracy of secondary data analysis.Transactions on Data Privacy202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/152391reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésTransactions on Data Privacy, 2025, 18(1)info:eu-repo/grantAgreement/MICINN/2021/PID2021-123637NB-I00info:eu-repo/grantAgreement/AEI/2021/10.13039info:eu-repo/grantAgreement/EC/H2020-871042/© the Authorsinfo:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1523912026-05-28T12:42:01Z
dc.title.none.fl_str_mv Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
title Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
spellingShingle Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
Soria-Comas, Jordi
individual differential privacy
machine learning
data microaggregation
data releases
title_short Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
title_full Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
title_fullStr Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
title_full_unstemmed Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
title_sort Conciliating privacy and utility in data releases via individual differential privacy and microaggregation
dc.creator.none.fl_str_mv Soria-Comas, Jordi
Sanchez, David
Domingo-Ferrer, Josep
Martinez, Sergio
Del Vasto-Terrientes, Luis
author Soria-Comas, Jordi
author_facet Soria-Comas, Jordi
Sanchez, David
Domingo-Ferrer, Josep
Martinez, Sergio
Del Vasto-Terrientes, Luis
author_role author
author2 Sanchez, David
Domingo-Ferrer, Josep
Martinez, Sergio
Del Vasto-Terrientes, Luis
author2_role author
author
author
author
dc.subject.none.fl_str_mv individual differential privacy
machine learning
data microaggregation
data releases
topic individual differential privacy
machine learning
data microaggregation
data releases
description ε-Differential privacy (DP) is a well-known privacy model that offers strong privacy guarantees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data releases have used weak privacy parameters (large ε), which dilute the privacy guarantees of DP. In this work, we tackle this issue by using an alternative formulation of the DP privacy guarantees, named ε-individual differential privacy (iDP), which causes less data distortion while providing the same protection as DP to subjects. We enforce iDP in data releases by relying on attribute masking plus a pre-processing step based on data microaggregation. The goal of this step is to reduce the sensitivity to record changes, which determines the amount of noise required to enforce iDP (and DP). Specifically, we propose data microaggregation strategies designed for iDP whose sensitivities are significantly lower than those used in DP. As a result, we obtain iDP-protected data with significantly better utility than with DP. We report on experiments that show how our approach can provide strong privacy (small ε) while yielding protected data that do not significantly degrade the accuracy of secondary data analysis.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
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 http://hdl.handle.net/10609/152391
url http://hdl.handle.net/10609/152391
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Transactions on Data Privacy, 2025, 18(1)
info:eu-repo/grantAgreement/MICINN/2021/PID2021-123637NB-I00
info:eu-repo/grantAgreement/AEI/2021/10.13039
info:eu-repo/grantAgreement/EC/H2020-871042/
dc.rights.none.fl_str_mv © the Authors
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © the Authors
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Transactions on Data Privacy
publisher.none.fl_str_mv Transactions on Data Privacy
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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