We prove that $\mathbb {F} _p $ sketch, a well-celebrated streaming algorithm for frequency
moments estimation, is differentially private as is when $ p\in (0, 1] $. $\mathbb {F} _p …

The streaming model of computation is a popular approach for working with large-scale
data. In this setting, there is a stream of items and the goal is to compute the desired …

In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR) for locally
differentially private (LDP) frequency estimation. For universe size of k and with n users, our …

Abstract Local Differential Privacy (LDP) protects user privacy from the data collector. LDP
protocols have been increasingly deployed in the industry. A basic building block is …

Local Differential Privacy (LDP) is the de facto standard technique to ensure privacy for
users whose data is collected by a data aggregator they do not necessarily trust. This …

Local Differential Privacy (LDP) allows answering queries on users data while maintaining
their privacy. Queries are often issued on multidimensional datasets with categorical and …

We provide optimal lower bounds for two well-known parameter estimation (also known as
statistical estimation) tasks in high dimensions with approximate differential privacy. First, we …

This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation
under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving …

Private collection of statistics from a large distributed population is an important problem,
and has led to large scale deployments from several leading technology companies. The …

Sketches are widely used for frequency estimation of data with a large domain. However,
sketches-based frequency estimation faces more challenges when considering privacy …