With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

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

We study the problem of estimating $ k $-ary distributions under $\eps $-local differential
privacy. $ n $ samples are distributed across users who send privatized versions of their …

We present novel, computationally efficient, and differentially private algorithms for two
fundamental high-dimensional learning problems: learning a multivariate Gaussian and …

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 …

Linear regression is one of the most prevalent techniques in machine learning; however, it is
also common to use linear regression for its explanatory capabilities rather than label …

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 …

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 …

We study the problem of distribution testing when the samples can only be accessed using a
locally differentially private mechanism and focus on two representative testing questions of …