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 …

Many modern cities strive to integrate information technology into every aspect of city life to
create so-called smart cities. Smart cities rely on a large number of application areas and …

The Gaussian mechanism is an essential building block used in multitude of differentially
private data analysis algorithms. In this paper we revisit the Gaussian mechanism and show …

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

The wide-spread availability of rich data has fueled the growth of machine learning
applications in numerous domains. However, growth in domains with highly-sensitive data …

For many differentially private algorithms, such as the prominent noisy stochastic gradient
descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training …

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 promises to enable general data analytics while protecting individual
privacy, but existing differential privacy mechanisms do not support the wide variety of …

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 …

Despite recent widespread deployment of differential privacy, relatively little is known about
what users think of differential privacy. In this work, we seek to explore users' privacy …