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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorLarrea Lizartza, Xabat
dc.contributor.authorAlberdi Aramendi, Ane
dc.contributor.otherHernandez, Mikel
dc.contributor.otherEpelde, Gorka
dc.contributor.otherBeristain, Andoni
dc.contributor.otherMolina, Cristina
dc.contributor.otherRankin, Debbie
dc.contributor.otherBamidis, Panagiotis
dc.contributor.otherKonstantinidis, Evdokimos
dc.date.accessioned2023-03-20T15:01:35Z
dc.date.available2023-03-20T15:01:35Z
dc.date.issued2022
dc.identifier.issn2673-4591en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=171200en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6047
dc.description.abstractA large amount of health and well-being data is collected daily, but little of it reaches its research potential because personal data privacy needs to be protected as an individual’s right, as reflected in the data protection regulations. Moreover, the data that do reach the public domain will typically have under-gone anonymization, a process that can result in a loss of information and, consequently, research potential. Lately, synthetic data generation, which mimics the statistics and patterns of the original, real data on which it is based, has been presented as an alternative to data anonymization. As the data collected from health and well-being activities often have a temporal nature, these data tend to be time series data. The synthetic generation of this type of data has already been analyzed in different studies. However, in the healthcare context, time series data have reduced research potential without the subjects’ metadata, which are essential to explain the temporal data. Therefore, in this work, the option to generate synthetic subjects using both time series data and subject metadata has been analyzed. Two approaches for generating synthetic subjects are proposed. Real time series data are used in the first approach, while in the second approach, time series data are synthetically generated. Furthermore, the first proposed approach is implemented and evaluated. The generation of synthetic subjects with real time series data has been demonstrated to be functional, whilst the generation of synthetic subjects with synthetic time series data requires further improvements to demonstrate its viability.en
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2022 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjecttime seriesen
dc.subjectsynthetic dataen
dc.subjectshareable dataen
dc.subjectPrivacyen
dc.titleSynthetic Subject Generation with Coupled Coherent Time Series Data †en
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceEngineering Proceedingsen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/engproc2022018007en
local.contributor.otherinstitutionhttps://ror.org/0023sah13en
local.contributor.otherinstitutionhttps://ror.org/01a2wsa50es
local.contributor.otherinstitutionhttps://ror.org/01yp9g959en
local.contributor.otherinstitutionhttps://ror.org/02j61yw88en
local.contributor.otherinstitutionhttps://ror.org/05vpgt980en
local.source.detailsVol. 18. N. 1en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
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
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