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Using the five safes to structure economic evaluations of data governance (2024)
Journal Article
Ritchie, F., & Whittard, D. (2024). Using the five safes to structure economic evaluations of data governance. Data & Policy, 6, Article e16

As the world has become more digitally-dependent, questions of data governance such as ethics, institutional arrangements and statistical protection measures have increased in significance. Understanding the economic contribution of investments in da... Read More about Using the five safes to structure economic evaluations of data governance.

The inadvertently revealing statistic: A systemic gap in statistical training? (2024)
Journal Article
Derrick, B., Green, E., Ritchie, F., Smith, J., & White, P. (2024). The inadvertently revealing statistic: A systemic gap in statistical training?. Significance, 21(1), 24-27. https://doi.org/10.1093/jrssig/qmae009

While concerns around data privacy are well-known, there's a lack of awareness and training when it comes to the confidentiality risk of published statistics, argue Ben Derrick, Elizabeth Green, Felix Ritchie, Jim Smith, Paul White

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.

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.

10 is the safest number that there's ever been (2022)
Journal Article
Ritchie, F. (2022). 10 is the safest number that there's ever been. Transactions on data privacy, 15(2), 109-140

When checking frequency and magnitude tables for disclosure risk, the cell threshold (the minimum number of observations in each cell) is a crucial parameter. In rules-based environments, this is a hard limit on what can or can't be published. In pri... Read More about 10 is the safest number that there's ever been.