As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

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 …

Federated Learning (FL) is a machine learning framework where many clients
collaboratively train models while kee** the training data decentralized. Despite recent …

Linear sketches have been widely adopted to process fast data streams, and they can be
used to accurately answer frequency estimation, approximate top K items, and summarize …

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

Estimating the density of a distribution from samples is a fundamental problem in statistics. In
many practical settings, the Wasserstein distance is an appropriate error metric for density …

Policymakers often rely on official statistics and administrative data to make essential public
policy decisions, such as using administrative tax data to broaden our understanding of …

In this work we study the problem of differentially private (DP) quantiles, in which given
dataset $ X $ and quantiles $ q_1,..., q_m\in [0, 1] $, we want to output $ m $ quantile …

Federal administrative data, such as tax data, are invaluable for research, but because of
privacy concerns, access to these data is typically limited to select agencies and a few …

In this work we consider the problem of differentially private computation ofquantiles for the
data, especially the highest quantiles such as maximum, butwith an unbounded range for …