All Outputs (4)

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.

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.