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Other literature type . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Project deliverable . 2025
License: CC BY
Data sources: Datacite
ZENODO
Project deliverable . 2025
License: CC BY
Data sources: Datacite
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HealthData@EU Pilot - Deliverable 8.3 Recommendations on application of privacy enhancing technologies, data security and node compute capabilities

Authors: Health Data Hub;

HealthData@EU Pilot - Deliverable 8.3 Recommendations on application of privacy enhancing technologies, data security and node compute capabilities

Abstract

This deliverable, D8.3, is part of Work Package 8 (WP8) of the HealthData@EU Pilot, a project that concluded on December 31, 2024. The HealthData@EU Pilot aimed to test the implementation of the European Health Data Space (EHDS) infrastructure for the secondary use of health data, supporting research, innovation, policymaking, and regulatory activities. Objective D8.3 provides recommendations on the application of Privacy Enhancing Technologies (PETs), data security measures, and compute capabilities. It builds on insights from the five use cases (UCs) of the pilot, assessing different approaches to data access, privacy protection, and computing requirements. Approach The analysis follows the HealthData@EU Data Life Cycle, as defined in the TEHDAS Joint Action, and includes: Privacy Enhancing Technologies (PETs) and data security: Overview of threats and risks. Prevention and mitigation strategies. Implementation of security measures in each use case. Federated and centralised data access models: All use cases employed federated analysis, with variations between “Data visiting” and “Federated analysis by proxy analyst.” The ECDC use case piloted additional scenarios, including centralised pooled analysis and federated querying (aligned with EHDS Article 69). Compute capabilities: Hardware and software requirements gathered from use cases. Consideration of EHDS requirements for applicants to specify computational resources needed for data access. Key Findings Diverse PETs adoption across use cases, with different security measures based on data sensitivity and processing needs. Varying approaches to federated and centralised data access, with implications for security and performance. Challenges in ensuring robust data security, particularly in cross-border collaborations. Computational resource requirements differ depending on the size and complexity of the datasets involved.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average