Privacy-preserving statistical data analysis addresses the general question of protecting
privacy when publicly releasing information about a sensitive dataset. A privacy attack takes …

Differential privacy has gained a lot of attention in recent years as a general model for the
protection of personal information when used and disclosed for secondary purposes. It has …

In federated learning (FL), model performance typically suffers from client drift induced by
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …

Deep learning based on artificial neural networks is a very popular approach to modeling,
classifying, and recognizing complex data such as images, speech, and text. The …

We develop and study multicalibration as a new measure of fairness in machine learning
that aims to mitigate inadvertent or malicious discrimination that is introduced at training time …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

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 …

Working under a model of privacy in which data remain private even from the statistician, we
study the tradeoff between privacy guarantees and the risk of the resulting statistical …

Working under local differential privacy-a model of privacy in which data remains private
even from the statistician or learner-we study the tradeoff between privacy guarantees and …

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 …