Automatic Checking of Research Outputs (ACRO): A tool for dynamic disclosure checks (2021)
Journal Article
Green, E., Ritchie, F., & Smith, J. (2021). Automatic Checking of Research Outputs (ACRO): A tool for dynamic disclosure checks. ESS Statistical Working Papers, 2021 Edition, https://doi.org/10.2785/75954

This paper discusses the issues surrounding the creation of an automatic tool to reduce the burden of output checking in research environments. It describes ACRO (Automatic Checking of Research Outputs), a Stata tool written as a proof-of-concept, an... Read More about Automatic Checking of Research Outputs (ACRO): A tool for dynamic disclosure checks.

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.

Security properties of light clients on the ethereum blockchain (2020)
Journal Article
Paavolainen, S., & Carr, C. (2020). Security properties of light clients on the ethereum blockchain. IEEE Access, 8, 124339-124358. https://doi.org/10.1109/access.2020.3006113

Ethereum is a decentralized blockchain, known as being the second most popular public blockchain after Bitcoin. Since Ethereum is decentralised the canonical state is determined by the Ethereum network participants via a consensus mechanism without a... Read More about Security properties of light clients on the ethereum blockchain.

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.

10 is the safest number that there’s ever been (2019)
Presentation / Conference
Ritchie, F. (2019, October). 10 is the safest number that there’s ever been. Paper presented at Workshop on statistical data confidentiality 2019, The Hague

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

User-focused threat identification for anonymised microdata (2019)
Journal Article
Ritchie, F., Hafner, H., & Lenz, R. (2019). User-focused threat identification for anonymised microdata. Statistical Journal of the IAOS, 35(4), 703-713. https://doi.org/10.3233/SJI-190506

© 2019 - IOS Press and the authors. When producing anonymised microdata for research, national statistics institutes (NSIs) identify a number of 'risk scenarios' of how intruders might seek to attack a confidential dataset. This approach has been cri... Read More about User-focused threat identification for anonymised microdata.