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 …

Privacy-preserving data publishing is an important problem that has been the focus of
extensive study. The state-of-the-art solution for this problem is differential privacy, which …

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

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 …

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 …

The objective of machine learning is to extract useful information from data, while privacy is
preserved by concealing information. Thus it seems hard to reconcile these competing …

\epsilon-differential privacy is the state-of-the-art model for releasing sensitive information
while protecting privacy. Numerous methods have been proposed to enforce epsilon …

We introduce a universal framework for characterizing the statistical efficiency of a statistical
estimation problem with differential privacy guarantees. Our framework, which we call High …

We consider\emphdifferentially private algorithms for convex empirical risk minimization
(ERM). Differential privacy (Dwork et al., 2006b) is a recently introduced notion of privacy …