Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/93821
DC FieldValueLanguage
dc.contributor.authorSilva, Paulo-
dc.contributor.authorGoncalves, Carolina-
dc.contributor.authorGodinho, Carolina-
dc.contributor.authorAntunes, Nuno-
dc.contributor.authorCurado, Marília-
dc.date.accessioned2021-03-20T09:54:54Z-
dc.date.available2021-03-20T09:54:54Z-
dc.date.issued2020-
dc.identifier.isbn978-1-7281-8695-5-
dc.identifier.issn978-1-7281-8695-5 (eISSN)-
dc.identifier.issn978-1-7281-8696-2-
dc.identifier.urihttps://hdl.handle.net/10316/93821-
dc.description.abstractPrivacy concerns are constantly increasing in different sectors. Regulations such as the EU's General Data Protection Regulation (GDPR) are pressuring organizations to handle the individual's data with reinforced caution. As information systems deal with increasingly large amounts of personal data in essential services, there is a lack of mechanisms to help organizations in protecting the involved data subjects. In this paper, we propose and evaluate the use of Named Entity Recognition as a way to identify, monitor and validate Personally Identifiable Information. In our experiments, we used three of the most well-known Natural Language Processing tools (NLTK, Stanford CoreNLP, and spaCy). First, we assess the effectiveness of the tools with a generic dataset. Then, machine learning models are trained and evaluated with datasets built on data that contain personally identifiable information. The results show that models' performance was highly positive in accurately classifying both generic and more context-specific data. We observe the relationship between the datasets' training size and respective performance and estimate the appropriate size for model training within this context. Furthermore, we discuss how our proposal can effectively act as a Privacy Enhancing Technology as well as the potential risks and associated impacts.-
dc.language.isoeng-
dc.publisherIEEE-
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/786713/EU/Protection and control of Secured Information by means of a privacy enhanced Dashboard-
dc.rightsopenAccess-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/-
dc.titleUsing NLP and Machine Learning to Detect Data Privacy Violations-
dc.typearticle-
degois.publication.firstPage972-
degois.publication.lastPage977-
degois.publication.locationToronto-
degois.publication.titleIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)-
dc.relation.publisherversionhttps://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162683-
dc.peerreviewedyes-
dc.identifier.doi10.1109/INFOCOMWKSHPS50562.2020.9162683-
dc.date.embargo2021-12-31*
uc.date.periodoEmbargo730-
item.openairetypearticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0001-6760-4675-
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais
Files in This Item:
Show simple item record

SCOPUSTM   
Citations

6
checked on Nov 11, 2022

Page view(s)

166
checked on Mar 26, 2024

Download(s)

692
checked on Mar 26, 2024

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons