65 citations · 114 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Private Federated Multiclass Post-hoc Calibration
Samuel Maddock, Graham Cormode, Carsten Maple
Calibrating machine learning models so that predicted probabilities better reflect the true outcome frequencies is crucial for reliable decision-making across many applications. In…
Synthetic Tabular Data: Methods, Attacks and Defenses
Graham Cormode, Samuel Maddock, Enayat Ullah +1
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much…
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…
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
Samuel Maddock, Alexandre Sablayrolles, Pierre Stock
Federated Learning (FL) is a setting for training machine learning models in distributed environments where the clients do not share their raw data but instead send model updates t…