Outputs (7)

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.

Risk of disclosure when reporting commonly used univariate statistics (2022)
Conference Proceeding
Derrick, B., Green, E., Ritchie, F., & White, P. (2022). Risk of disclosure when reporting commonly used univariate statistics. In Lecture Notes in Computer Science (119-129). https://doi.org/10.1007/978-3-031-13945-1_9

When basic or descriptive summary statistics are reported, it may be possible that the entire sample of observations is inadvertently disclosed, or that members within a sample will be able to work out responses of others. Three sets of univariate su... Read More about Risk of disclosure when reporting commonly used univariate statistics.

Disclosure risks in odds ratios and logistic regression (2022)
Presentation / Conference
Derrick, B., Green, E., Ritchie, F., & White, P. (2022, April). Disclosure risks in odds ratios and logistic regression. Paper presented at Scottish Economic Society Annual Conference 2022: Special session 'Protecting confidentiality in social science research outputs', Glasgow

When publishing statistics from confidential data, there exists a risk that the statistic might inadvertently reveal confidential information. Statistical disclosure control (SDC) aims to reduce that risk to an acceptable level. Most SDC theory is co... Read More about Disclosure risks in odds ratios and logistic regression.

Safety in numbers: Minimum thresholding, Maximum bounds and Little White Lies : The case of the mean and standard deviation (2022)
Presentation / Conference
Derrick, B., Green, E., Kember, K., Ritchie, F., & White, P. (2022, April). Safety in numbers: Minimum thresholding, Maximum bounds and Little White Lies : The case of the mean and standard deviation. Paper presented at Scottish Economic Society, Glasgow

Reporting the sample mean, sample standard deviation and sample size could in some cases lead to the unique identification of the underpinning sample. The likelihood of this reveal via direct enumeration of the possible search space decreases with i... Read More about Safety in numbers: Minimum thresholding, Maximum bounds and Little White Lies : The case of the mean and standard deviation.

Statistical disclosure controls for machine learning models (2021)
Conference Proceeding
Krueger, S., Mansouri-Benssassi, E., Ritchie, F., & Smith, J. (2021). Statistical disclosure controls for machine learning models

Artificial Intelligence (AI) models are trained on large datasets. Where the training data is sensitive, the data holders need to consider risks posed by access to the training data and risks posed by the models that are released. The first problem c... Read More about Statistical disclosure controls for machine learning models.

Understanding output checking (2020)
Report
Green, E., Ritchie, F., & Smith, J. (2020). Understanding output checking. Luxembourg: European Commission (Eurostat - Methodology Directorate)

This report for Eurostat (Methodology) considers the conceptual and practical issues that need to be addressed in designing and implementing automatic disclosure control checking for statistical research outputs. The report covers - The basic theo... Read More about Understanding output checking.

Confidentiality and linked data (2018)
Book Chapter
Ritchie, F., & Smith, J. Confidentiality and linked data. In G. Roarson (Ed.), Privacy and Data Confidentiality Methods – a National Statistician’s Quality Review (1-34). Newport: Office for National Statistics

This chapter considers the confidentiality issues around linked data. It notes that the use and availability of secondary (adminstrative or social media) data, allied to powerful processing and machine learning techniques, in theory means that re-ide... Read More about Confidentiality and linked data.