Este artigo foi apresentado no evento 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2017

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 Cooperation Programme, Horizon...

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 Cooperation Programme, Horizon 2020 grant agreement no 690116. Also, it is supported by the project DEVASSES (www.devasses.eu), funded by the European Union’s FP7 under grant agreement no PIRSES-GA-2013-612569. UFMG team is also partially supported by CNPq, FAPEMIG, as well as projects InWeb and MASWeb

Abstract: 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 exposed through the web....

Abstract: 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 exposed through the web. Organizations care about data privacy as it directly affects the confidence that clients have that their personal data are safe. This paper presents a data privacy approach - PRIVAaaS - and its inte-gration to the LEMONADE Web-based platform, developed to compose ETL (Extract, Transform, Load) process and Machine Learning workflows. The 3-level approach of PRIVAaaS, based on data anonymization policies, is implemented in a software toolkit that provides a set of libraries and tools which allows controlling and reducing data leakage in the context of Big Data processing

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG

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