56 citations · 197 across the 22 of their papers we have counts for
7 papers · 1 filter
Federated Calibration and Evaluation of Binary Classifiers
Graham Cormode, Igor Markov
We address two major obstacles to practical use of supervised classifiers on distributed private data. Whether a classifier was trained by a federation of cooperating clients or tr…
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…
Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms
Saeed Mahloujifar, Alexandre Sablayrolles, Graham Cormode +1
Given a trained model and a data sample, membership-inference (MI) attacks predict whether the sample was in the model's training set. A common countermeasure against MI attacks is…
Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy
Mary Scott, Graham Cormode, Carsten Maple
Advances in communications, storage and computational technology allow significant quantities of data to be collected and processed by distributed devices. Combining the informatio…
Applying the Shuffle Model of Differential Privacy to Vector Aggregation
Mary Scott, Graham Cormode, Carsten Maple
In this work we introduce a new protocol for vector aggregation in the context of the Shuffle Model, a recent model within Differential Privacy (DP). It sits between the Centralize…
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
Graham Cormode, Igor L. Markov
Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of so…