The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

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 …

We give new upper and lower bounds on the minimax sample complexity of differentially
private mean estimation of distributions with bounded $ k $-th moments. Roughly speaking …

Differentially private analysis of graphs is widely used for releasing statistics from sensitive
graphs while still preserving user privacy. Most existing algorithms however are in a …

We present two sample-efficient differentially private mean estimators for $ d $-dimensional
(sub) Gaussian distributions with unknown covariance. Informally, given $ n\gtrsim d/\alpha …

We provide a differentially private algorithm for hypothesis selection. Given samples from an
unknown probability distribution $ P $ and a set of $ m $ probability distributions $\mathcal …

We develop lower bounds for estimation under local privacy constraints—including
differential privacy and its relaxations to approximate or Rényi differential privacy—by …

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

The simplest and most widely applied method for guaranteeing differential privacy is to add
instance-independent noise to a statistic of interest that is scaled to its global sensitivity …

We propose conducting locally differentially private (LDP) estimation with the aid of a small
amount of public data to enhance the performance of private estimation. Specifically, we …