All Outputs (11)

Machine learning models in trusted research environments - Understanding operational risks (2023)
Journal Article
Ritchie, F., Tilbrook, A., Cole, C., Jefferson, E., Krueger, S., Mansouri-Benssassi, E., …Smith, J. (2023). Machine learning models in trusted research environments - Understanding operational risks. International Journal of Population Data Science, 8(1), Article 2165. https://doi.org/10.23889/ijpds.v8i1.2165

IntroductionTrusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amou... Read More about Machine learning models in trusted research environments - Understanding operational risks.

Using pedagogical and psychological insights to train analysts using confidential data (2023)
Journal Article
Green, E., & Ritchie, F. (2023). Using pedagogical and psychological insights to train analysts using confidential data. Journal of Privacy and Confidentiality, 13(2), https://doi.org/10.29012/jpc.842

With researchers increasingly gaining access to confidentiality data through restricted environments, interest has grown in the training of those researchers to protect confidentiality and to use the secure facility effectively. Researcher training,... Read More about Using pedagogical and psychological insights to train analysts using confidential data.

The present and future of the Five Safes framework (2023)
Journal Article
Green, E., & Ritchie, F. (2023). The present and future of the Five Safes framework. Journal of Privacy and Confidentiality, 13(2), https://doi.org/10.29012/jpc.831

The Five Safes has become the default framework for confidential data governance across multiple sectors and countries. Since its inception in 2003, the approach has influenced data management in many ways, particularly in the public sector. As it ha... Read More about The present and future of the Five Safes framework.

SACRO guide to statistical output checking (2023)
Other
Ritchie, F., Green, E., Smith, J., Tilbrook, A., & White, P. (2023). SACRO guide to statistical output checking. [web]

This guide for output SDC is the first report from the SACRO project. It covers, theory of output SDC, including the new statbarns model, practicalities, operational considerations, and FAQs for output checking teams.

Research data governance in low-and middle-income countries (2023)
Report
Ferrer Breda, P., Green, E., Kendal, C., & Ritchie, F. (2023). Research data governance in low-and middle-income countries. Bristol: UWE

Research and policy development on the governance of confidential research data is dominated by the work of academics and government agencies based in high-income countries (HICs). This leaves three quarters of the world’s population faced with a cor... Read More about Research data governance in low-and middle-income countries.

Disclosure control issues in complex medical data (2023)
Presentation / Conference
Green, E., Ritchie, F., Smith, J., Western, D., & White, P. (2023, September). Disclosure control issues in complex medical data. Paper presented at UNECE/Eurostat Expert Group on Statisticial Data Confidentiality, Wiesbaden

The covid19 pandemic assisted the acceleration of routine access to medical records for research. In the UK platforms including OpenSafely and NHSDigital, alongside emerging hospital trust based Trusted Research Environments (TREs), demonstrate the u... Read More about Disclosure control issues in complex medical data.

Towards a comprehensive theory and practice of output SDC (2023)
Presentation / Conference
Derrick, B., Green, E., Ritchie, F., & White, P. (2023, September). Towards a comprehensive theory and practice of output SDC. Paper presented at UNECE/Eurostat Expert Group on Statisticial Data Confidentiality, Wiesbaden

In 2000, the statistical disclosure control of outputs (OSDC) was largely limited to models of table protection developed by and intended for national statistical institutes (NSIs), as a particular branch of general SDC theory. However, in this centu... Read More about Towards a comprehensive theory and practice of output SDC.

Research data governance in low- and middle-income countries (2023)
Presentation / Conference
Ferrer Breda, P., Green, E., & Ritchie, F. (2023, September). Research data governance in low- and middle-income countries. Paper presented at UNECE/Eurostat Expert Group on Statisticial Data Confidentiality, Wiesbaden

Research and policy development on the governance of confidential research data is dominated by the work of academics and government agencies based in high-income countries (HICs). This leaves three quarters of the world’s population faced with a cor... Read More about Research data governance in low- and middle-income countries.

SACRO: Semi-Automated Checking Of Research Outputs (2023)
Presentation / Conference
Smith, J., Preen, R., Albashir, M., Ritchie, F., Green, E., Davy, S., …Bacon, S. (2023, September). SACRO: Semi-Automated Checking Of Research Outputs. Paper presented at UNECE Expert meeting on Statistical Data Confidentiality, Wiesbaden, Germany

Output checking can require significant resources, acting as a barrier to scaling up the research use of confidential data. We report on a project, SACRO, that is developing a general-purpose, semi-automatic output checking systems that works across... Read More about SACRO: Semi-Automated Checking Of Research Outputs.

The perils of pre-filling: Lessons from the UK's Annual Survey of Hours and Earning microdata (2023)
Journal Article
Whittard, D., Ritchie, F., Phan, V., Bryson, A., Forth, J., Stokes, L., & Singleton, C. (2023). The perils of pre-filling: Lessons from the UK's Annual Survey of Hours and Earning microdata. Statistical Journal of the IAOS, 39(3), 661-677. https://doi.org/10.3233/SJI-230013

The role of the National Statistical Institution (NSI) is changing, with many now making microdata available to researchers through secure research environments This provides NSIs with an opportunity to benefit from the methodological input from rese... Read More about The perils of pre-filling: Lessons from the UK's Annual Survey of Hours and Earning microdata.

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities (2023)
Journal Article
Mansouri-Benssassi, E., Rogers, S., Reel, S., Malone, M., Smith, J., Ritchie, F., & Jefferson, E. (2023). Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities. Heliyon, 9(4), Article e15143. https://doi.org/10.1016/j.heliyon.2023.e15143

Introduction: Artificial intelligence (AI) applications in healthcare and medicine have increased in recent years. To enable access to personal data, Trusted Research Environments (TREs) (otherwise known as Safe Havens) provide safe and secure enviro... Read More about Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.