We consider training models with differential privacy (DP) using mini-batch gradients. The
existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD) …

Matrix factorization (MF) mechanisms for differential privacy (DP) have substantially
improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in a …

We study fine-grained error bounds for differentially private algorithms for counting under
continual observation. Our main insight is that the matrix mechanism when using lower …

Motivated by recent applications requiring differential privacy in the setting of adaptive
streams, we investigate the question of optimal instantiations of the matrix mechanism in this …

The first large-scale deployment of private federated learning uses differentially private
counting in the continual release model as a subroutine (Google AI blog titled “Federated …

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 …

We introduce new differentially private (DP) mechanisms for gradient-based machine
learning (ML) with multiple passes (epochs) over a dataset, substantially improving the …

Differentially private learning algorithms inject noise into the learning process. While the
most common private learning algorithm, DP-SGD, adds independent Gaussian noise in …

In privacy under continual observation we study how to release differentially private
estimates based on a dataset that evolves over time. The problem of releasing private prefix …

We study fine-grained error bounds for differentially private algorithms for counting under
continual observation. Our main insight is that the matrix mechanism when using lower …