Publication:
Trading accuracy for privacy in machine learning tasks: an empirical analysis

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorProdomo, Vittorio
dc.contributor.authorGonzález Sánchez, Roberto
dc.contributor.authorGramaglia, Marco
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-04-19T12:36:22Z
dc.date.available2022-04-19T12:36:22Z
dc.date.issued2022-03-22
dc.descriptionProceeding of: IEEE Conference on Communications and Network Security (CNS 2021), 4-6 Oct. 2021, Tempe, AZ, USA (Virtual conference)en
dc.description.abstractDifferent kinds of user-generated data are increasingly used to tailor and optimize, through Machine Learning, the operation of online services and infrastructures. This typically requires sharing data among different partners, often including private data of individuals or business confidential data. While this poses privacy issues, the current state-of-the-art solutions either impose strong assumptions on the usage scenario or drastically reduce the data quality. In this paper, we evaluate through a generic framework the trade-offs between the accuracy of Machine Learning tasks and the achieved privacy (measured as similarity) on the input data, discussing trends and ways forward.en
dc.description.sponsorshipThe work of University Carlos III of Madrid was supported by the H2020 5G-TOURS project (grant no. 856950).en
dc.format.extent2 p.es
dc.identifier.bibliographicCitationGonzález Sánchez, Roberto; Gramaglia, Marco; Prodomo, Vittorio. Trading accuracy for privacy in machine learning tasks: an empirical analysis. In: IEEE Conference on Communications and Network Security (CNS 2021). IEEE, March 2022, 2 p.en
dc.identifier.doihttps://doi.org/10.1109/CNS53000.2021.9729036
dc.identifier.isbn978-1-6654-4496-5
dc.identifier.publicationfirstpage1es
dc.identifier.publicationlastpage2es
dc.identifier.publicationtitle2021 IEEE Conference on Communications and Network Security (CNS)en
dc.identifier.urihttps://hdl.handle.net/10016/34568
dc.identifier.uxxiCC/0000032636
dc.language.isoengen
dc.publisherIEEEes
dc.relation.eventdate2021-10-04es
dc.relation.eventplaceTempe, AZ, USA (Virtual conference)en
dc.relation.eventtitleIEEE Conference on Communications and Network Security (CNS 2021)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856950/5G-TOURSen
dc.rights© 2021 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherMachine Learninges
dc.subject.otherPrivacyen
dc.subject.otherTrade offsen
dc.titleTrading accuracy for privacy in machine learning tasks: an empirical analysisen
dc.typeconference poster*
dc.type.hasVersionAM*
dspace.entity.typePublication
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