3 papers
cs.LG2025
GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks
Samuel Maddock, Shripad Gade, Graham Cormode +1
State-of-the-art differentially private synthetic tabular data has been defined by adaptive 'select-measure-generate' frameworks, exemplified by methods like AIM. These approaches…
cs.LG2025
PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning
Luca Melis, Matthew Grange, Iden Kalemaj +4
The increasing deployment of Machine Learning (ML) models in sensitive domains motivates the need for robust, practical privacy assessment tools. PrivacyGuard is a comprehensive to…
cs.LG2025
Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
Samuel Maddock, Shripad Gade, Graham Cormode +1
Differentially Private Synthetic Data Generation (DP-SDG) is a key enabler of private and secure tabular-data sharing, producing artificial data that carries through the underlying…