47 citations · 182 across the 18 of their papers we have counts for
4 papers · 1 filter
Predicting Privacy Leakage from Weight Spectral Density
Richard J. Preen, Jim Smith
Membership inference attacks (MIAs) are widely used to audit the privacy disclosure risk of machine learning models, however current state-of-the-art attacks require training compu…
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…
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…
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
Emily Jefferson, James Liley, Maeve Malone +16
TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure a…