3 papers
cs.LG2025
Safe machine learning model release from Trusted Research Environments: The SACRO-ML package
Jim Smith, Richard J. Preen, Andrew McCarthy +8
We present SACRO-ML, an integrated suite of open source Python tools to facilitate the statistical disclosure control (SDC) of machine learning (ML) models trained on confidential…
cs.LG2025
A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack
Richard J. Preen, Jim Smith
Machine learning models can inadvertently expose confidential properties of their training data, making them vulnerable to membership inference attacks (MIA). While numerous evalua…
cs.CR2025
A multi-language toolkit for the semi-automated checking of research outputs
Richard J. Preen, Maha Albashir, Simon Davy +1
This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. S…