Privacy pentagon in Big Data Analytics: theoretical model proposal

Detalhes bibliográficos
Autor(a) principal: Lopes, Brenner
Data de Publicação: 2024
Outros Autores: Barbosa, Ricardo Rodrigues, Falcão, Luander Cipriano de Jesus, Souza, Renato Rocha
Tipo de documento: Artigo
Idioma: por
Título da fonte: Revista Informação na Sociedade Contemporânea
Texto Completo: https://periodicos.ufrn.br/informacao/article/view/33898
Resumo: We live in an environment characterized as an ocean of data, which grows not only in terms of volume and quantity, but also in terms of variety, being created and moving at high speed. Currently, structured data is much smaller in quantity and importance, and adjustments and improvements in technologies and analytical models were partly carried out to adapt to this new reality, which is known as Big Data Analytics. One of the issues of great concern in this new reality are threats to privacy. The question raised as a result of several research is that the procedures, techniques, technologies, and legislation currently available cannot fully guarantee privacy. Given this complex scenario, the objective of this research was to propose a multifaceted theoretical model within the scope of Big Data Analytics, which guarantees privacy, while not making its extraction of value unfeasible. The methodology proposed for this work was supported by the systematic literature review approach, with a view to critically analyzing the notes and conclusions of previous studies, identifying, and logically proposing new hypotheses and constructs, in order to format the final design of a theoric model. As a result, the Pentagon of Privacy in Big Data Analytics is proposed, which includes a kaleidoscope of solutions capable of guaranteeing privacy while guaranteeing the extraction of value in Big Data Analytics. The construct obtained as a result of this work provides a concise and consistent answer to the starting question of this work.
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spelling Privacy pentagon in Big Data Analytics: theoretical model proposalPentágono da privacidade no Big Data Analytics: proposta de modelo teóricoprivacidadebig data analyticsbig data privacyvalor do big datapentágono da privacidadeprivacybig data analyticsbig data privacyvalue of big dataprivacy pentagonWe live in an environment characterized as an ocean of data, which grows not only in terms of volume and quantity, but also in terms of variety, being created and moving at high speed. Currently, structured data is much smaller in quantity and importance, and adjustments and improvements in technologies and analytical models were partly carried out to adapt to this new reality, which is known as Big Data Analytics. One of the issues of great concern in this new reality are threats to privacy. The question raised as a result of several research is that the procedures, techniques, technologies, and legislation currently available cannot fully guarantee privacy. Given this complex scenario, the objective of this research was to propose a multifaceted theoretical model within the scope of Big Data Analytics, which guarantees privacy, while not making its extraction of value unfeasible. The methodology proposed for this work was supported by the systematic literature review approach, with a view to critically analyzing the notes and conclusions of previous studies, identifying, and logically proposing new hypotheses and constructs, in order to format the final design of a theoric model. As a result, the Pentagon of Privacy in Big Data Analytics is proposed, which includes a kaleidoscope of solutions capable of guaranteeing privacy while guaranteeing the extraction of value in Big Data Analytics. The construct obtained as a result of this work provides a concise and consistent answer to the starting question of this work.Vivemos num ambiente caracterizado como um oceano de dados, que cresce não só quanto ao seu volume e quantidade, mas também em termos de variedade, sendo criado e transitando em alta velocidade. Atualmente os dados estruturados estão em quantidade e importância bem menor, e os ajustes e aprimoramentos nas tecnologias e modelos analíticos foram em parte realizados para se adaptarem a essa nova realidade, que convencionou-se chamar de Big Data Analytics. Entre as questões de grande preocupação, nessa nova realidade, estão as ameaças à privacidade. A questão posta como resultado de diversas pesquisas é que os procedimentos, técnicas, tecnologias e legislações, atualmente disponíveis, não conseguem dar garantia plena à privacidade. Diante desse complexo cenário, o objetivo dessa pesquisa foi propor um modelo teórico multifacetado no âmbito do Big Data Analytics, que garanta a privacidade, ao mesmo tempo em que não inviabilize sua extração de valor. A metodologia proposta para esse trabalho foi a revisão sistemática da literatura, com vistas à análise crítica dos apontamentos e conclusões de estudos anteriores, a identificação e proposição lógica de novas hipóteses e construtos, de maneira a formatar o desenho final de um modelo teórico. Como resultado é proposto o Pentágono da Privacidade no Big Data Analytics, que contempla um caleidoscópio de soluções capazes de garantir a privacidade ao mesmo tempo que dá garantias à extração de valor no Big Data Analytics. O construto obtido como resultado desse trabalho, traz uma resposta concisa e consistente à questão de partida desse trabalho.Portal de Periódicos Eletrônicos da UFRN2024-01-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufrn.br/informacao/article/view/3389810.21680/2447-0198.2024v8n1ID33898Revista Informação na Sociedade Contemporânea; Vol. 8 (2024): Janeiro-Dezembro; e33898Revista Informação na Sociedade Contemporânea; Vol. 8 (2024): Janeiro-Dezembro; e33898Revista Informação na Sociedade Contemporânea; v. 8 (2024): Janeiro-Dezembro; e338982447-019810.21680/2447-0198.2024v8n1reponame:Revista Informação na Sociedade Contemporâneainstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNporhttps://periodicos.ufrn.br/informacao/article/view/33898/18193Copyright (c) 2024 Brenner Lopes, Ricardo Rodrigues Barbosa, Luander Falcão, Renato Rocha Souzahttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessLopes, BrennerBarbosa, Ricardo Rodrigues Falcão, Luander Cipriano de JesusSouza, Renato Rocha 2024-01-13T22:37:52Zoai:periodicos.ufrn.br:article/33898Revistahttps://periodicos.ufrn.br/informacao/indexPUBhttps://periodicos.ufrn.br/informacao/oairisc.ufrn@gmail.com ; risc.decin@gmail.com2447-01982447-0198opendoar:2024-01-13T22:37:52Revista Informação na Sociedade Contemporânea - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.none.fl_str_mv Privacy pentagon in Big Data Analytics: theoretical model proposal
Pentágono da privacidade no Big Data Analytics: proposta de modelo teórico
title Privacy pentagon in Big Data Analytics: theoretical model proposal
spellingShingle Privacy pentagon in Big Data Analytics: theoretical model proposal
Lopes, Brenner
privacidade
big data analytics
big data privacy
valor do big data
pentágono da privacidade
privacy
big data analytics
big data privacy
value of big data
privacy pentagon
title_short Privacy pentagon in Big Data Analytics: theoretical model proposal
title_full Privacy pentagon in Big Data Analytics: theoretical model proposal
title_fullStr Privacy pentagon in Big Data Analytics: theoretical model proposal
title_full_unstemmed Privacy pentagon in Big Data Analytics: theoretical model proposal
title_sort Privacy pentagon in Big Data Analytics: theoretical model proposal
author Lopes, Brenner
author_facet Lopes, Brenner
Barbosa, Ricardo Rodrigues
Falcão, Luander Cipriano de Jesus
Souza, Renato Rocha
author_role author
author2 Barbosa, Ricardo Rodrigues
Falcão, Luander Cipriano de Jesus
Souza, Renato Rocha
author2_role author
author
author
dc.contributor.author.fl_str_mv Lopes, Brenner
Barbosa, Ricardo Rodrigues
Falcão, Luander Cipriano de Jesus
Souza, Renato Rocha
dc.subject.por.fl_str_mv privacidade
big data analytics
big data privacy
valor do big data
pentágono da privacidade
privacy
big data analytics
big data privacy
value of big data
privacy pentagon
topic privacidade
big data analytics
big data privacy
valor do big data
pentágono da privacidade
privacy
big data analytics
big data privacy
value of big data
privacy pentagon
description We live in an environment characterized as an ocean of data, which grows not only in terms of volume and quantity, but also in terms of variety, being created and moving at high speed. Currently, structured data is much smaller in quantity and importance, and adjustments and improvements in technologies and analytical models were partly carried out to adapt to this new reality, which is known as Big Data Analytics. One of the issues of great concern in this new reality are threats to privacy. The question raised as a result of several research is that the procedures, techniques, technologies, and legislation currently available cannot fully guarantee privacy. Given this complex scenario, the objective of this research was to propose a multifaceted theoretical model within the scope of Big Data Analytics, which guarantees privacy, while not making its extraction of value unfeasible. The methodology proposed for this work was supported by the systematic literature review approach, with a view to critically analyzing the notes and conclusions of previous studies, identifying, and logically proposing new hypotheses and constructs, in order to format the final design of a theoric model. As a result, the Pentagon of Privacy in Big Data Analytics is proposed, which includes a kaleidoscope of solutions capable of guaranteeing privacy while guaranteeing the extraction of value in Big Data Analytics. The construct obtained as a result of this work provides a concise and consistent answer to the starting question of this work.
publishDate 2024
dc.date.none.fl_str_mv 2024-01-13
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufrn.br/informacao/article/view/33898
10.21680/2447-0198.2024v8n1ID33898
url https://periodicos.ufrn.br/informacao/article/view/33898
identifier_str_mv 10.21680/2447-0198.2024v8n1ID33898
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.ufrn.br/informacao/article/view/33898/18193
dc.rights.driver.fl_str_mv Copyright (c) 2024 Brenner Lopes, Ricardo Rodrigues Barbosa, Luander Falcão, Renato Rocha Souza
https://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Brenner Lopes, Ricardo Rodrigues Barbosa, Luander Falcão, Renato Rocha Souza
https://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Portal de Periódicos Eletrônicos da UFRN
publisher.none.fl_str_mv Portal de Periódicos Eletrônicos da UFRN
dc.source.none.fl_str_mv Revista Informação na Sociedade Contemporânea; Vol. 8 (2024): Janeiro-Dezembro; e33898
Revista Informação na Sociedade Contemporânea; Vol. 8 (2024): Janeiro-Dezembro; e33898
Revista Informação na Sociedade Contemporânea; v. 8 (2024): Janeiro-Dezembro; e33898
2447-0198
10.21680/2447-0198.2024v8n1
reponame:Revista Informação na Sociedade Contemporânea
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Revista Informação na Sociedade Contemporânea
collection Revista Informação na Sociedade Contemporânea
repository.name.fl_str_mv Revista Informação na Sociedade Contemporânea - Universidade Federal do Rio Grande do Norte (UFRN)
repository.mail.fl_str_mv risc.ufrn@gmail.com ; risc.decin@gmail.com
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