Building trust in health data management: Master’s thesis highlights STRONG-AYA’s leading role

July 31, 2025

Building trust in health data management: Master’s thesis highlights STRONG-AYA’s leading role

Flor Pasturino, master’s student from the Higher School of Studies “Ferdinando Rossi”, has recently completed her thesis that shines a spotlight on a critical challenge: how to manage consent for health data in a way that fosters trust and protects participants’ autonomy. Supervised by Marine Jacquemin from our partner Maastricht University, her thesis “Towards a Trust-Based Consent Model for Health Data Management: Insights from the STRONG-AYA Project” uses STRONG-AYA as a case study to explore how new models of consent can be developed for data-driven, cross-border biomedical research.

Rethinking Consent in a Data-Driven World

In her thesis, Flor Pasturino argues that traditional consent forms, often lengthy and filled with legal jargon, are no longer fit for purpose in a rapidly changing landscape of health research. Big data and machine learning offer the potential for earlier diagnoses, more personalised treatments, and better outcomes — especially for vulnerable or rare patient groups like adolescents and young adults (AYA) with cancer. But these new opportunities also bring ethical and legal challenges for processing and sharing patient data for research purposes. Simply providing more information to patients is not enough: Trust is needed not only between an individual patient and their doctor, but also across researchers, hospitals, and institutions that handle sensitive personal (health) data.

The STRONG-AYA Case: Federated Learning and Trust

Aiming to improve cancer outcomes in AYAs by connecting different data nodes in Europe in a federated infrastructure, STRONG-AYA provided the ideal case study for this thesis to investigate a Trust-Based Consent (TBC) model for managing health data. Because AYAs represent a relatively small patient group, sharing data between countries is essential for advancing research in this population — but it also raises concerns about privacy and data protection.

The thesis describes how the STRONG-AYA Federated Learning (FL) architecture is a privacy-enhancing technique that allows hospitals and other data holders to keep control of their data locally while only sharing aggregated results. The thesis shows that this setup can be further strengthened through a Trust-Based Consent (TBC) model, where consent is viewed not as a one-time form, but as an ongoing, reciprocal relationship between participants, clinicians and researchers based on transparency, safeguards and ethical oversight.

A Role for a Trusted Third Party

One of the thesis’ key proposals is the creation of an independent Trusted Third Party (TTP) within the STRONG-AYA project. This neutral body would maintain a single consent registry, screen data access requests, manage participant withdrawals, and serve as a recognisable contact point for participants. This aligns with recent European legislation such as the Data Governance Act and the European Health Data Space, which highlight the need for trust-enhancing mechanisms in health data sharing.

By embedding a TTP into its governance structure and formalizing ethical data sharing in policy documents, STRONG-AYA — with its federated infrastructure and patient-centric approach — could become a model for other (rare) disease networks and research collaborations. As the thesis concludes: “by integrating safeguards, transparent dialogue, and rigorous oversight at every level of its architecture, TBC in STRONG-AYA shapes a future in which citizens can contribute to shaping the collective good generated by their sensitive data, thus transforming trust into a decisive factor of data-driven biomedical research.”

The STRONG-AYA consortium congratulates Flor on her successful thesis and graduation with honors (cum laude). We look forward to continuing the conversation on how STRONG-AYA can set an example for responsible and trust-driven cross-border research.