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 …

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 …

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 …

Privacy amplification exploits randomness in data selection to provide tighter differential
privacy (DP) guarantees. This analysis is key to DP-SGD's success in machine learning, but …

We study the problem of maintaining a differentially private decaying sum under continual
observation. We give a unifying framework and an efficient algorithm for this problem for any …

Differentially private weighted prefix sum under continual observation is a crucial component
in the production-level deployment of private next-word prediction for Gboard, which …

The hereditary discrepancy of a set system is a certain quantitative measure of the
pseudorandom properties of the system. Roughly, hereditary discrepancy measures how …

Motivated by privacy concerns in long-term longitudinal studies in medical and social
science research, we study the problem of continually releasing differentially private …