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The potential of big data in health research is dependent upon the ability to collect, reuse, link, and analyse those datasets. The EU General Data Protection Regulation 2016/679 presents big data health research with several difficulties and uncertainties. In this Article we analyze the impact of five elements of the GDPR on big data health research: (1) the distinction made between non-special versus special categories of data; (2) informed consent and its relation to secondary processing of data; (3) the use of the national identification number; (4) the principle of data minimization; and, (5) the principle of storage limitation. We argue that, while the GDPR relaxes its multiple restrictions in the case of scientific research, (1) the GDPR continues to leave too much discretionary power to the Member States, thereby undermining the goal of harmonization; and that (2) the GDPR does not adequately address the challenges brought by the emergence of big data and artificial intelligence used in health research. To conclude, we will advocate for a GDPR Article 40 code of conduct for scientific research to mitigate the issues arising from the GDPR. [ABSTRACT FROM AUTHOR]

Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data. [ABSTRACT FROM AUTHOR]

Little is known about the way in which different loyalty program attributes underlie consumers' intentions to participate in such a program. Based upon equity theory, the current study distinguished between consumer inputs (personal data release, participation cost, purchase frequency, participation exclusivity, and participation efforts) and outputs (program benefits, number of program providers, and program duration) as underlying attributes potentially affecting participation in a loyalty program. Using conjoint analysis, we explored how different levels within each of these eight attributes affect consumers' intentions to participate. The study holds major implications for the design of successful customer loyalty programs. [ABSTRACT FROM PUBLISHER]

It is widely anticipated that the use and analysis of health-related big data will enable further understanding and improvements in human health and wellbeing. Here, we propose an innovative infrastructure, which supports secure and privacy-preserving analysis of personal health data from multiple providers with different governance policies. Our objective is to use this infrastructure to explore the relation between Type 2 Diabetes Mellitus status and healthcare costs. Our approach involves the use of distributed machine learning to analyze vertically partitioned data from the Maastricht Study, a prospective population-based cohort study, and data from the official statistics agency of the Netherlands, Statistics Netherlands (Centraal Bureau voor de Statistiek; CBS). This project seeks an optimal solution accounting for scientific, technical, and ethical/legal challenges. We describe these challenges, our progress towards addressing them in a practical use case, and a simulation experiment.