Call for Papers

Special Issue: Privacy Enhancing Technologies (PETs) for Population-Scale Data Use

 

The International Journal of Population Data Science (IJPDS) invites submissions for a special issue dedicated to Privacy Enhancing Technologies (PETs) for Population-Scale Data Use.

The growing availability of large-scale, sensitive population data—especially in health, social care, and the public sector—offers unprecedented opportunities for research and evidence-based policymaking. However, leveraging these data require robust and scalable mechanisms to protect individual privacy and maintain public trust. This special issue seeks to gather cutting-edge research, practical implementations, and critical analyses concerning the use of PETs to safely and ethically unlock the value of population data.

We encourage submissions that explore the theoretical foundations, methodological advancements, and real-world application of PETs in contexts involving large, complex, and confidential datasets. Topics of interest include, but are not limited to, the following:

Key Areas of Focus

  • Differential/Formal Privacy: Novel mechanisms, practical implementations, utility-privacy trade-offs, and deployment challenges for differential/formal privacy in population datasets.
  • Synthetic Data: Methods for generating high-utility synthetic data (e.g., fully synthetic, partially synthetic) from sensitive population sources, including validation, bias assessment, and privacy guarantees.
  • Secure Multi-Party Computation (Secure MPC): Use cases and protocols for collaborative analysis across multiple data custodians without revealing underlying private data, focusing on scalability for population data.
  • Privacy-Preserving Record Linkage (PPRL): Advancements in cryptographic and non-cryptographic PPRL techniques, evaluation of linkage accuracy, and deployment in cross-sectoral data integration.
  • Trusted Research Environments (TREs) / Data Safe Havens: Practical models for secure access and analysis of population data, including technical architectures, operational best practices, innovative security controls and federation of TREs for multi-site analyses.
  • Governance, Compliance, and Real-World Implementations:
    • Case studies and evaluations of PETs in operational settings (e.g., national statistics offices, health data systems, social services).
    • Frameworks and policies for the governance, legal compliance, and ethical oversight of PET deployment.
    • Assessment of user acceptance, public engagement, and trust implications related to PETs.
    • Methodological challenges in applying PETs to real-world, messy, and non-ideal population data.

We welcome original research articles, reviews, case studies, and perspective pieces that contribute to the theoretical and practical understanding of how PETs can enable data-intensive research while safeguarding individual privacy at population scale.

Submission Details

Call for Papers Opens  

1 April, 2026

Submission Deadline

30 November, 2026

Submissions must be made through the IJPDS online submission system, clearly indicating the intent to submit to the "Special Issue on Privacy Enhancing Technologies (PETs)" in the cover letter. All manuscripts will undergo the journal’s standard rigorous peer-review process. Please see  Author Guidelines | International Journal of Population Data Science for details on submissions.

 

Special Issue Editors

Amy O'Hara, PhD is a research professor at Georgetown University in the Massive Data Institute of the McCourt School of Public Policy and executive director of the university's Federal Statistical Research Data Center.  She works on data governance, record linkage, and privacy enhancing technologies. She has published on topics including the measurement of income, longitudinal linkages to measure economic mobility, and the data infrastructure necessary to support government and academic research. She is president of the Association of Public Data Users. (See also: https://mdi.georgetown.edu/faculty-spotlight/mdi-faculty-spotlight-professor-amy-ohara/)

Rob Baxter, PhD is National Digital Infrastructure Technical Lead on the DARE UK Programme. His day job involves figuring out how the UK can connect all its Trusted Research Environments together in a coherent and self-consistent way. Before he joined DARE UK he was at EPCC, the high-performance computing centre at the University of Edinburgh, for nearly three decades, latterly as Director of Data Services. And once upon a time he used to be a theoretical physicist. (See also https://www.linkedin.com/in/rob-baxter/)

Claire McKay Bowen, PhD is a senior fellow at the Urban Institute. Her research focuses on developing technical and policy solutions to safely expand access to confidential data for advancing evidence-based policymaking and ensuring everyone is responsibly represented in data. She is also an American Statistical Association Fellow, a member of the ICPSR Governing Council, and adjunct professor at Stonehill College.

(See also: https://clairemckaybowen.com/about/)

Abel Kho, MD is an Internist and Professor of Medicine and Preventive Medicine in the Northwestern University Feinberg School of Medicine and Founding Director of the Institute for Artificial Intelligence in Medicine (2020).  His research focuses on developing novel methods to link and analyze diverse data (e.g. electronic health records, administrative data, geospatial data) and the real-world application of privacy enhancing technologies.   He is an elected Fellow of the American College of Medical Informatics and recipient of the Donald A.B. Lindbergh Award for Innovation in Informatics. (See also: https://www.feinberg.northwestern.edu/faculty-profiles/az/profile.html?xid=16090)

Ngan MacDonald, MS is a speaker and data management leader focused on the ethical and practical use of AI and data in healthcare. As the Chief of Data Operations at the Institute for AI in Medicine at Northwestern University, she advocates for the use of AI to augment human decision making. At Mathematica, she leads the Data Innovation Lab which combines human expertise with deep access to a broad spectrum of data. With over 2 decades of experience in the data space from the commercial, governmental and academic environments, she brings an understanding that true data collaborations need to be cross-sector. (See also: https://www.linkedin.com/in/nganmacdonald/)