12 citations · 13 across the 4 of their papers we have counts for
4 papers
Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity
Mary Scott, Graham Cormode, Carsten Maple
Statistical heterogeneity is a measure of how skewed the samples of a dataset are. It is a common problem in the study of differential privacy that the usage of a statistically het…
Federated and differentially private estimation of KL divergence
Sayan Biswas, Graham Cormode, Carsten Maple +1
Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytics applications. In many practica…
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…