Health Data Research Network (HDRN) Canada is a distributed network of organizations working together to transform multi-regional health data access and analysis. Central to this strategy is the development and implementation of analytical methods that enable researchers to conduct multi-regional studies within the complexities of Canada’s federated data environment, where direct sharing of line-level data across jurisdictions is generally not permitted.
Distributed analysis allows statistical analyses to be performed across multiple sites without sharing line-level data. Instead, intermediate privacy-protected numerical results are exchanged between local sites through a coordinating node. This approach respects privacy and data-governance requirements and enables the inclusion of data that cannot otherwise be moved or pooled due to legal, ethical, policy, or social-acceptability constraints.
To advance distributed analytics in Canada, HDRN Canada piloted three methods (linear, logistic, and Cox regression) for horizontally distributed data across organizations. Despite demonstrating feasibility, hesitation remains among network members, data custodians, and privacy officers regarding data protection and privacy preservation. Continued assessment and demonstration of minimal risk is essential for strengthening confidence and trust. HDRN Canada is co-developing clear governance mechanisms with its members and data custodians to support responsible implementation and to build the foundation for distributed analytics across Canada.
As the network matures, HDRN Canada continues to expand participation and the analytical techniques available. Future directions include piloting vertical distributions, building governance structures to support scalable implementation, and cultivating broader awareness of distributed analytics. Through its collaborations, HDRN Canada is fostering innovation in multi-regional health data use across Canada.
Distributed analytics enables analyses to be conducted when data is partitioned across multiple sites and cannot be pooled in one location. The primary motivation for using these methods is to protect confidentiality of line-level data. Distributed analytics methods rely on exchanging intermediate numerical outputs generated locally at each participating site. However, the absence of line-level data exchange should not be mistaken for privacy protection.
To address this, the GRIIS developed a framework for evaluating privacy, helping users distinguish different levels of privacy preservation when applying distributed methods. This work was especially important, as earlier research demonstrated that an existing distributed method could be reverse-engineered to recover line-level data, which underscores that the distributed nature of an approach does not guarantee data privacy.
In the framework, we define data privacy as guaranteed if line-level data cannot be uniquely recovered from the numerical outputs exchanged or disclosed during the execution of the approach. By eliciting line-level data as a set of unknown variables subject to constraints derived from the procedure, we can, in some settings, mathematically prove that privacy is protected under this definition. Achieving this depends on the nature of the variables included (binary, continuous), the results shared at the end of the procedure and the availability of external information.
Ultimately, this work plays a critical role in supporting the responsible use of distributed analytics. By establishing governance mechanisms to evaluate and mitigate privacy risks, it helps ensure that these methods can be adopted appropriately and at scale across the country.
International data transfer rules, designed to protect individuals, often create barriers to collaborative research by imposing constraints misaligned with modern data ecosystems. Frameworks like GDPR and UK GDPR can unintentionally hinder scientific progress by failing to recognise the safeguards provided by emerging technologies. More nuanced legal approaches are needed to preserve privacy while enabling responsible international research.
We will examine how privacy-enhancing technologies (PETs) can help address these challenges. Informed by insights from pilot projects under the Alzheimer’s Disease Data Initiative which seek to address critical dementia questions, while expanding dataset access. With dementia cases projected to rise globally from 57 million to 153 million by 2050, this work demonstrates the urgent need for cross-border data sharing in brain health research.
Specifically, we will explore how PETs offer pathways through regulatory barriers, and how Trusted Research Environments (TREs), aligned with the Five Safes Framework, provide strong safeguards to prevent identifiable data disclosure. We will demonstrate how remote querying techniques enable international analysis without data transfers, as researchers receive only aggregate results.
The presentation will address persistent challenges: under European Data Protection Board guidance, even viewing data across borders constitutes a transfer, meaning TRE access may trigger complex compliance requirements despite technical safeguards. We will explore the rapidly evolving landscape through new UK legislation, the European Health Data Space, and emerging case law. Finally, we will discuss whether PETs can bridge the gap between privacy protection and scientific progress, and what regulatory adaptations are required to recognise the protections they provide.
As sensitive data become increasingly central to research addressing societal challenges - particularly in health, social sciences, and public policy - there is an urgent need for infrastructure that enables secure, ethical, and streamlined access across institutional and national boundaries. Federated models for data access via Trusted Research Environments (TREs) offer a promising pathway, allowing data to remain under the control of its custodians while providing researchers with the tools and permissions needed to analyse data remotely under controlled conditions. Our talk presents work being conducted as part of the EOSC-ENTRUST project, which is aiming to establish a European network of TREs and developing a federated framework for identity and access management (IAM) across European research infrastructures. Through the testing of two SANE-Pilot Prototypes, we explore how federated access to TREs could help overcome key challenges such as: (1) Policy and legal constraints; (2) Equivalence issues; and (3) Resource constraints. Further, we will look into the user experience of researchers engaging with federated TREs. These will be evaluated through real-world use cases involving researchers seeking access to sensitive data under varying national governance frameworks, and testing TINKER (GESIS) and TINKER and BLIND SANE (UKDS Pilot).