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Stretching the Limit, The Functioning of the GDPR’s Notion of Consent in the context of Data Intermediary Services


Yannick Alexander Vogel


This work is distributed under the Creative Commons Licence Attribution 4.0 International (CC BY 4.0).

The European Union strives to keep its Data Economy competitive and fit for the future. The proclamation of data as ‘new oil’ requires the envisioning of new ways to make this ‘oil’ available to data-driven industries. The recently adopted Data Governance Act (DGA) is a tool that increases the possibility of data-flows towards data driven industries, while simultaneously promising to maintain uncompromised data protection standards for individuals. The DGA sets the legislative framework for Data Sharing Services or Data Intermediaries. These services stand in between data subjects and data users, and serve as actor that make demand- and supply sides of data meet. When handling personal data, the Data Governance Act pivots on several notions from the GDPR, for instance that of consent. In doing so, it becomes questionable whether or not the notion of consent functions, in the DGA, in the manner as it was envisioned to function in the GDPR. A strict reading of the notion of consent makes its application in the structure of the Data Governance Act difficult to image for reasons explored in this paper. Most pressing are the elements that make up the notion of consent. Those elements being that consent should be specific, freely given and informed. These three elements are put under strain in the DGA’s multi-party, data-pool, or data exchange relationships. This paper highlights how the Data Governance Act states its measures are designed to ‘fully’ respect the GDPR as starting point. However, when examining the notion of consent, true GDPR compliance may be an unobtainable goal or at least an unscalable one in some contexts of the Data Intermediary Services.
Keywords: Consent | Data Governance Act | Data Intermediaries | Data Pooling | Data Holders | Data Users | European Strategy for Data

PhD Student, University of Bologna. For Correspondence: <>. The author wishes to thank those who provided valuable feedback on drafts of this work and those who participated in the discussion during the Young Scholars Award during the CPDP 2022 Conference. Special gratitude goes out to Prof. M. Durante, Prof. W. Wendehorst and Prof. N. Forgó. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ITN EJD grant agreement No 814177.


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