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 …

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 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 …

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 …

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 …

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

In this paper, we consider the $ k $-approximate pattern matching problem under differential
privacy, where the goal is to report or count all substrings of a given string $ S $ which have …

We present a polynomial-time algorithm for online differentially private synthetic data
generation. For a data stream within the hypercube $[0, 1]^ d $ and an infinite time horizon …