Data analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe-Brazil Collaboration of Big Data Scientic Research Through Cloud-Centric Applications), we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, trac jam estimation and trip recommendation based on estimated crowdedness. All developments are available under Open Source licenses (

Privacy concerns are constantly increasing in different sectors. Regulations such as the EU's General Data Protection Regulation (GDPR) are pressuring organizations to handle the individual's data with reinforced caution. As information systems deal with increasingly large amounts of personal data in essential services, there is a lack of mechanisms to help organizations in protecting the involved data subjects.In this paper, we propose and evaluate the use of Named Entity Recognition as a way to identify, monitor and validate Personally Identifiable Information. In our experiments, we used three of the most well-known Natural Language Processing tools (NLTK, Stanford CoreNLP, and spaCy). First, we assess the effectiveness of the tools with a generic dataset. Then, machine learning models are trained and evaluated with datasets built on data that contain personally identifiable information.The results show that models' performance was highly positive in accurately classifying both generic and more context-specific data. We observe the relationship between the datasets' training size and respective performance and estimate the appropriate size for model training within this context. Furthermore, we discuss how our proposal can effectively act as a Privacy Enhancing Technology as well as the potential risks and associated impacts.

As information systems deal with contracts and documents in essential services, there is a lack of mechanisms to help organizations in protecting the involved data subjects. In this paper, we evaluate the use of named entity recognition as a way to identify, monitor and validate personally identifiable information. In our experiments, we use three of the most well-known Natural Language Processing tools (NLTK, Stanford CoreNLP, and spaCy). First, the effectiveness of the tools is evaluated in a generic dataset. Then, the tools are applied in datasets built based on contracts that contain personally identifiable information. The results show that models' performance was highly positive in accurately classifying both the generic and the contracts' data. Furthermore, we discuss how our proposal can effectively act as a Privacy Enhancing Technology.

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Recent developments in information technology such as the Internet of Things and the cloud computing paradigm enable public and private organisations to collect large amounts of data to employ various data analytic techniques for extracting important information that helps improve their businesses. Unfortunately, these benefits come with a high cost in terms of privacy exposures given the high sensitivity of the data that are usually processed at powerful third-party servers. Given the ever-increasing of data breaches, the serious damage they cause, and the need for compliance to the European General Data Protection Regulation (GDPR), these organisations look for secure and privacy-preserving data handling practices. During the workshop, we aimed at presenting an approach to the problem of user data protection and control, currently being developed in the scope of the PoSeID-on and PAPAYA H2020 European projects.