PRIVAaaS : privacy approach for a distributed cloud-based data analytics platforms
Tania Basso, Regina Moraes, Nuno Antunes, Marco Vieira, Walter Santos, Meira Jr. Wagner
ARTIGO
Inglês
Agradecimentos: This work has been partially supported by the project EUBra-BIGSEA (www.eubra-bigsea.eu), funded by the Brazilian Ministry of Science, Technology and Innovation (Project 23614 - MCTI/RNP 3rd Coordinated Call) and by the European Commission under the Coope
Data privacy is a key challenge that is exacerbated by Big Data storage and analytics processing requirements. Big Data and Cloud Computing are related and allow the users to access data from any device, making data privacy essential as the data sets are
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG
Fechado
DOI: https://doi.org/10.1109/CCGRID.2017.136
Texto completo: https://ieeexplore.ieee.org/document/7973820
PRIVAaaS : privacy approach for a distributed cloud-based data analytics platforms
Tania Basso, Regina Moraes, Nuno Antunes, Marco Vieira, Walter Santos, Meira Jr. Wagner
PRIVAaaS : privacy approach for a distributed cloud-based data analytics platforms
Tania Basso, Regina Moraes, Nuno Antunes, Marco Vieira, Walter Santos, Meira Jr. Wagner
Fontes
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2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (July, 2017), p. 1108-1116 |