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Tecnologias de perfilamento e dados agregados de geolocalização no combate à COVID-19 no Brasil: uma análise dos riscos individuais e coletivos à luz da LGPD (Profiling Technologies and Aggregated Geolocation Data in the Fight against COVID-19 in Brazil: An Analysis of Individual and Collective Risks in the Light of the LGPD)

Authors: Diego Machado; Laura Schertel Mendes;

Tecnologias de perfilamento e dados agregados de geolocalização no combate à COVID-19 no Brasil: uma análise dos riscos individuais e coletivos à luz da LGPD (Profiling Technologies and Aggregated Geolocation Data in the Fight against COVID-19 in Brazil: An Analysis of Individual and Collective Risks in the Light of the LGPD)

Abstract

Portuguese Abstract: O presente trabalho visa analisar os riscos a privacidade e a protecao de dados pessoais – nas suas dimensoes individual e coletiva – gerados pelo perfilamento baseado no uso de dados agregados de geolocalizacao de dispositivos moveis, buscando investigar a existencia de parâmetros normativos encontrados na Lei Geral de Protecao de Dados (LGPD) aplicaveis aos riscos identificados. Para tanto, o artigo propoe as seguintes questoes de pesquisa: (i) quais riscos aos direitos fundamentais a privacidade e a protecao de dados pessoais tecnologias de perfilamento baseadas no uso de dados agregados de geolocalizacao de dispositivos moveis geram nos niveis individual e coletivo na luta contra a pandemia de COVID-19 no Brasil? (ii) a LGPD preve parâmetros normativos aplicaveis a fim de lidar com esses riscos, em especial a grupos criados a partir de sistemas algoritmicos? Na sociedade orientada por dados, o perfilamento automatizado tem importante funcao na infraestrutura da informacao e da comunicacao preponderante da computacao preemptiva (preemptive computing). Neste contexto, da-se a afirmacao da dimensao coletiva dos direitos a privacidade e a protecao de dados pessoais. Os riscos detectados a ambos direitos, inclusive no âmbito coletivo ou de grupo, sao o de reidentificacao dos usuarios de dispositivos moveis por ataques inferenciais (membership inference attacks) e de desvirtuamento de funcao e finalidade originaria do tratamento dos dados. A fim de lidar com tais riscos, sugere-se uma interpretacao sistematica de parâmetros normativos da LGPD, que tratam de perfilamento automatizado e de relatorio de impacto a protecao de dados pessoais. English Abstract: The present work aims to analyze the risks to privacy and to the protection of personal data - in both their individual and collective dimensions - generated by profiling based on the use of mobile phone geolocation aggregated data, seeking to investigate the existence of legal parameters found in the Brazilian General Data Protection Regulation (LGPD) and applicable to the identified risks. For this purpose, the present work proposes the following research questions: (i) what risks to the fundamental rights to privacy and to the protection of personal does data profiling technologies based on the use of geolocation aggregated data from mobile devices generate at the individual and collective levels in the fight against the COVID-19 pandemic in Brazil? (ii) Does the LGPD provide normative parameters applicable in order to deal with these risks, in particular to groups created from algorithmic systems? In a data-driven society, automated profiling plays out an important role in the information and communication infrastructure of preemptive computing. In this context, the collective dimension of both the right to privacy and the right to the protection of personal data is affirmed. The identified risks to data protection and privacy, including at the collective or group level, are both re-identification of mobile phone users through inferencial attacks (membership inference attacks) and function creep of the data processing and its purpose. In order to deal with such risks, it is suggested a systematical interpretation of LGPD legal parameters, regarding automated profiling and data protection impact assessments.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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