Privacy pentagon in Big Data Analytics: theoretical model proposal
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , |
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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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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1797067922256953344 |