As aging and very expensive programs put more pressure on health and social care systems, an increase in the need for electronic healthcare records can be observed, because they promise massive savings and better clinical quality. However, patients and commissioners for data protection have legitimate concerns about the privacy and confidentiality of the stored data. Although the concept of pseudonymization allows an association with a patient only under specified and controlled circumstances, existing approaches have major vulnerabilities. This paper provides a new architecture for the pseudonymization of medical data that combines primary and secondary use in one system and thus provides a solution to vulnerabilities of existing approaches.

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The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address crosssectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

A large number of EU organisations already leverage Big Data pools to drive value and investments. This trend also applies to the banking sector. As a specific example, CaixaBank currently manages more than 300 different data sources (more than 4 PetaBytes of data and increasing), and more than 700 internal and external active users and services are processing them every day. In order to harness value from such high-volume and high-variety of data, banks need to resolve several challenges, such as finding efficient ways to perform Big Data analytics and to provide solutions that help to increase the involvement of bank employees, the true decision-makers. In this chapter, we describe how these challenges are resolved by the self-service solution developed within the I-BiDaaS project. We present three CaixaBank use cases in more detail, namely, (1)analysis of relationships through IP addresses, (2)advanced analysis of bank transfer payment in financial terminalsand (3)Enhanced control of customers in online banking, and describe how the corresponding requirements are mapped to specific technical and business KPIs. For each use case, we present the architecture, data analysis and visualisation provided by the I-BiDaaS solution, reporting on the achieved results, domain-specific impact and lessons learned.

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