The Jury of the 2022 Stefano Rodotà Award, formed by the members of the Bureau of the Committee of Convention 108, has sélected

• in the “PhD thesis “ category, Teresa Quintel for her PHD work on “Managing Migration Flows by Processing Personal Data within the Adequate Data Protection Instrument - Scoping Exercise between general and law enforcement data protection rules applicable to Third Country Nationals”
• in the “articles” category, Sabrina Nucciotti for her work on “European Health Data Sharing is on the wrong track - How the distributed machine learning system, Personal Health Train (PHT), can overcome the European privacy barriers to health data sharing for medical research”.

Teresa Quintel and Sabrina Nucciotti will present their work to the Committee of Convention 108 at its 43rd plenary meeting.

Teresa Quintel

Teresa Quintel is a Lecturer at the Maastricht European Centre on Privacy and Cybersecurity (ECPC), which she joined in July 2021. Prior to her appointment, Teresa was a PhD candidate at the University of Luxembourg and Uppsala University; where she was also engaged in teaching and moot court activities. Apart from her research and teaching activities, she worked in different projects on EU and Council of Europe level. She has published on various topics in the context of data protection legislation and related matters.

Her thesis looks at situations in which the criminalisation of non-EU citizens – so called third country nationals – may lead to a lowering of data protection standards of these individuals. Where third country nationals are treated as if they were criminals, for instance, due to their irregular status, authorities such as border guards or police authorities could apply the so-called Law Enforcement Directive. Because this Directive applies when the processing of personal data is carried out in the area of police and criminal justice, its rules are more lenient than the rules under the General Data Protection Regulation (GDPR). The assessment underlines how an increasing number of legislative acts inherently criminalise migrants, for example by streamlining law enforcement access to personal data of third country nationals stored in non-law enforcement databases. Hence, the analysis identifies situations where data protection rules originally established for law enforcement context may be applied in the area of migration both on national and on EU level. In her concluding remarks, Teresa proposes several amendments to the existing data protection laws that could contribute to a clearer delineation between general data protection rules and those that apply for law enforcement processing.

Sabrina Nucciotti

Sabrina Nucciotti is a master student at Trinity College Dublin currently pursuing an LLM in IT and IP law. She graduated from Maastricht University's European Law School in 2021 while simultaneously participating in the faculty's Honours program; during this time, she also spent a semester abroad at the University of Edinburgh and worked at GIRP, the European Healthcare Distribution Association, in Brussels. Her interests centre around European data protection and privacy law with a special emphasis on the implementation of new technologies, particularly in the healthcare sector.

Sharing and re-using health data has become increasingly crucial, especially in light of the current Covid-19 pandemic. Indeed, health data is essential for medical research to improve diagnosis and prognosis, as well as provide better healthcare and personalised treatments. However, its sharing is not only nuanced but also difficult in practice: both health data itself and underlying data protection rules are highly fragmented across stakeholders and states. This Bachelor’s dissertation investigates the potential of distributed machine learning technologies vis-à-vis cloud-based initiatives to overcome European data protection restrictions and thus share health data more freely, interoperably, and transparently, but also as a means to increase data subjects’ involvement. It does so through a case study of the Personal Health Train (PHT), a distributed machine learning system used by Maastricht University and other research institutes to construct a survival prediction model for lung cancer patients.