Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

Local differential privacy is a widely studied restriction on distributed algorithms that collect
aggregates about sensitive user data, and is now deployed in several large systems. We …

It is well-known that general secure multi-party computation can in principle be applied to
implement differentially private mechanisms over distributed data with utility matching the …

Abstract Le Cam's method, Fano's inequality, and Assouad's lemma are three widely used
techniques to prove lower bounds for statistical estimation tasks. We propose their …

We study the power of interactivity in local differential privacy. First, we focus on the
difference between fully interactive and sequentially interactive protocols. Sequentially …

Multiple players are each given one independent sample, about which they can only provide
limited information to a central referee. Each player is allowed to describe its observed …

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 …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

We study the fundamental problems of identity testing (goodness of fit), and closeness
testing (two sample test) of distributions over $ k $ elements, under differential privacy. While …