Putting the GDPR into Practice: Difficulties and Uncertainties Experienced in the Conduct of Big Data Health Research is a research paper published in European Data Protection Law Review (2021). On theSindex it has a DataRank of 0.312. It has been cited 7 times.
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. Keywords: GDPR | Big Data | Health Research | Data Protection | Privacy | Confidentiality
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.312
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.