Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity

The focus of data anonymity research by computer scientists is almost completely on methods with formal guarantees of anonymity, especially differential privacy. The usefulness of mechanisms with formal guarantees, however, has so far been disappointing. This article argues that computer scientists...

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Detalles Bibliográficos
Autor: Francis, Paul
Tipo de recurso: informe técnico
Fecha de publicación:2019
País:España
Institución:IE
Repositorio:Repositorio IE
OAI Identifier:oai:repositorio.ie.edu:20.500.14417/2767
Acceso en línea:https://doi.org/10.5281/zenodo.3731250
https://hdl.handle.net/20.500.14417/2767
Access Level:acceso abierto
Palabra clave:Data set
Conjuntos de datos
Analysis
Personal Data
Datos personales
Privacy
Privacidad
Marketing
Technology
Tecnología
General Data Protection Regulation
GDPR
Regulación General de Protección de Datos
RGPD
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oai_identifier_str oai:repositorio.ie.edu:20.500.14417/2767
network_acronym_str ES
network_name_str España
repository_id_str
spelling Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymityFrancis, PaulData setConjuntos de datosAnalysisAnalysisPersonal DataDatos personalesPrivacyPrivacidadMarketingTechnologyTecnologíaGeneral Data Protection RegulationGDPRRegulación General de Protección de DatosRGPDThe focus of data anonymity research by computer scientists is almost completely on methods with formal guarantees of anonymity, especially differential privacy. The usefulness of mechanisms with formal guarantees, however, has so far been disappointing. This article argues that computer scientists should be open to and encouraged to work on empirical data anonymization mechanisms as well—in much the same way that researchers work on both formal and empirical approaches to crypto. This article describes differential privacy and explains its benefits and shortcomings. It also describes a recently developed empirical data anonymization mechanism called Diffix, and describes how transparency and programs that incentivize white-hat attacks, such as bounty programs, can build understanding and confidence in empirical approaches. The article concludes that there is a need for both formal and empirical research on data anonymity.Data setConjuntos de datosAnalysisAnalysisPersonal DataDatos personalesPrivacyPrivacidadMarketingTechnologyTecnologíaGeneral Data Protection RegulationGDPRRegulación General de Protección de DatosRGPDIE Universityhttps://ror.org/02jjdwm75202420242019info:eu-repo/semantics/reportapplication/pdfapplication/pdfhttps://doi.org/10.5281/zenodo.3731250https://hdl.handle.net/20.500.14417/2767reponame:Repositorio IEinstname:IEInglésIE Center for the Governance of ChangeIE UniversityAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcodeinfo:eu-repo/semantics/openAccessoai:repositorio.ie.edu:20.500.14417/27672026-06-15T12:40:57Z
dc.title.none.fl_str_mv Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
title Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
spellingShingle Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
Francis, Paul
Data set
Conjuntos de datos
Analysis
Analysis
Personal Data
Datos personales
Privacy
Privacidad
Marketing
Technology
Tecnología
General Data Protection Regulation
GDPR
Regulación General de Protección de Datos
RGPD
title_short Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
title_full Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
title_fullStr Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
title_full_unstemmed Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
title_sort Privacy, Data and the Individual. Diferentially Data sets : formal vs empirical approaches to data anonymity
dc.creator.none.fl_str_mv Francis, Paul
author Francis, Paul
author_facet Francis, Paul
author_role author
dc.contributor.none.fl_str_mv https://ror.org/02jjdwm75
dc.subject.none.fl_str_mv Data set
Conjuntos de datos
Analysis
Analysis
Personal Data
Datos personales
Privacy
Privacidad
Marketing
Technology
Tecnología
General Data Protection Regulation
GDPR
Regulación General de Protección de Datos
RGPD
topic Data set
Conjuntos de datos
Analysis
Analysis
Personal Data
Datos personales
Privacy
Privacidad
Marketing
Technology
Tecnología
General Data Protection Regulation
GDPR
Regulación General de Protección de Datos
RGPD
description The focus of data anonymity research by computer scientists is almost completely on methods with formal guarantees of anonymity, especially differential privacy. The usefulness of mechanisms with formal guarantees, however, has so far been disappointing. This article argues that computer scientists should be open to and encouraged to work on empirical data anonymization mechanisms as well—in much the same way that researchers work on both formal and empirical approaches to crypto. This article describes differential privacy and explains its benefits and shortcomings. It also describes a recently developed empirical data anonymization mechanism called Diffix, and describes how transparency and programs that incentivize white-hat attacks, such as bounty programs, can build understanding and confidence in empirical approaches. The article concludes that there is a need for both formal and empirical research on data anonymity.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://doi.org/10.5281/zenodo.3731250
https://hdl.handle.net/20.500.14417/2767
url https://doi.org/10.5281/zenodo.3731250
https://hdl.handle.net/20.500.14417/2767
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IE Center for the Governance of Change
IE University
dc.rights.none.fl_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IE University
publisher.none.fl_str_mv IE University
dc.source.none.fl_str_mv reponame:Repositorio IE
instname:IE
instname_str IE
reponame_str Repositorio IE
collection Repositorio IE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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