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

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of
study: adversarial machine learning---the study of effective machine learning techniques …

Boosting is a general method for improving the accuracy of learning algorithms. We use
boosting to construct improved privacy-pre serving synopses of an input database. These …

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 …

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 …

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 …

In this article, we demonstrate that, ignoring computational constraints, it is possible to
release synthetic databases that are useful for accurately answering large classes of queries …