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

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 …

Concerns on location privacy frequently arise with the rapid development of GPS enabled
devices and location-based applications. While spatial transformation techniques such as …

Statistical agencies face a dual mandate to publish accurate statistics while protecting
respondent privacy. Increasing privacy protection requires decreased accuracy …

We prove new upper and lower bounds on the sample complexity of (ε, δ) differentially
private algorithms for releasing approximate answers to threshold functions. A threshold …

Differentially private algorithms for answering sets of predicate counting queries on a
sensitive database have many applications. Organizations that collect individual-level data …

We study trade-offs between accuracy and privacy in the context of linear queries over
histograms. This is a rich class of queries that includes contingency tables and range …

In this paper we study the problem of approximately releasing the cut function of a graph
while preserving differential privacy, and give new algorithms (and new analyses of existing …

The first large-scale deployment of private federated learning uses differentially private
counting in the continual release model as a subroutine (Google AI blog titled “Federated …

Hypothesis testing plays a central role in statistical inference, and is used in many settings
where privacy concerns are paramount. This work answers a basic question about privately …