In the task of differentially private (DP) continual counting, we receive a stream of increments
and our goal is to output an approximate running total of these increments, without revealing …

The most common algorithms for differentially private (DP) machine learning (ML) are all
based on stochastic gradient descent, for example, DP-SGD. These algorithms achieve DP …

Differential privacy (DP) and federated learning (FL) are combined as advanced privacy-
preserving methods when training on-device language models in production mobile …

The collection, storage, and use of sensitive data often requires a trade-off between
individual privacy and public utility. Differential privacy (DP) formalises this trade-off, for …