Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system
and the amount of protection offered by privacy-enhancing technologies. In this way, privacy …

We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-
scale pre-trained language models, which achieve the state-of-the-art privacy versus utility …

Modern smart homes are being equipped with certain renewable energy resources that can
produce their own electric energy. From time to time, these smart homes or microgrids are …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

Abstract “Concentrated differential privacy” was recently introduced by Dwork and Rothblum
as a relaxation of differential privacy, which permits sharper analyses of many privacy …

Convex empirical risk minimization is a basic tool in machine learning and statistics. We
provide new algorithms and matching lower bounds for differentially private convex …

Differential privacy is a theoretical framework for ensuring the privacy of individual-level data
when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

We present new practical local differentially private heavy hitters algorithms achieving
optimal or near-optimal worst-case error--TreeHist and Bitstogram. In both algorithms, server …