65 citations · 114 across the 8 of their papers we have counts for
8 papers
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…
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
Samuel Maddock, Graham Cormode, Carsten Maple
Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirr…
Federated Boosted Decision Trees with Differential Privacy
Samuel Maddock, Graham Cormode, Tianhao Wang +2
There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically…
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…
Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]
Graham Cormode, Samuel Maddock, Carsten Maple
Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The…