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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorAlberdi Aramendi, Ane
dc.contributor.authorLarrea Lizartza, Xabat
dc.contributor.otherHernandez, Mikel
dc.contributor.otherEpelde Unanue, Gorka
dc.contributor.otherBeristain, Andoni
dc.contributor.otherÁlvarez Sánchez, Roberto
dc.contributor.otherMolina Moreno, Cristina
dc.contributor.otherTimoleon, Michalis
dc.contributor.otherBamidis, Panagiotis
dc.contributor.otherKonstantinidis, Evdokimos
dc.date.accessioned2022-03-09T13:29:25Z
dc.date.available2022-03-09T13:29:25Z
dc.date.issued2022
dc.identifier.issn2079-9292en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167415en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5486
dc.description.abstractTo date, the use of synthetic data generation techniques in the health and wellbeing domain has been mainly limited to research activities. Although several open source and commercial packages have been released, they have been oriented to generating synthetic data as a standalone data reparation process and not integrated into a broader analysis or experiment testing workflow. In this context, the VITALISE project is working to harmonize Living Lab research and data capture protocols and to provide controlled processing access to captured data to industrial and scientific communities. In this paper, we present the initial design and implementation of our synthetic data generation approach in the context of VITALISE Living Lab controlled data processing workflow, together with identified challenges and future developments. By uploading data captured from Living Labs, generating synthetic data from them, developing analysis locally with synthetic data, and then executing them remotely with real data, the utility of the proposed workflow has been validated. Results have shown that the presented workflow helps accelerate research on artificial intelligence, ensuring compliance with data protection laws. The presented approach has demonstrated how the adoption of state-of-the-art synthetic data generation techniques can be applied for real-world applications.en
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2022 by the authors. Licensee MDPIen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectsynthetic data generationen
dc.subjectLiving Laben
dc.subjectcontrolled data processingen
dc.subjectmachine learningen
dc.titleIncorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domainen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceElectronicsen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/electronics11050812en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101007990/EU/VIrtual healTh And weLlbeing Living Lab InftraStructurE/VITALISEen
local.rights.publicationfeeAPCen
local.contributor.otherinstitutionhttps://ror.org/0023sah13es
local.contributor.otherinstitutionhttps://ror.org/02j61yw88en
local.contributor.otherinstitutionhttps://ror.org/05vpgt980en
local.source.detailsVol. 11. N. 5. N. artículo 812, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International