Vector mean estimation is a central primitive in federated analytics. In vector mean
estimation, each user i∈n holds a real-valued vector v_i∈-1,1^d, and a server wants to …

We consider the problem of computing differentially private approximate histograms and
heavy hitters in a stream of elements. In the non-private setting, this is often done using the …

Sketching is an important tool for dealing with high-dimensional vectors that are sparse (or
well-approximated by a sparse vector), especially useful in distributed, parallel, and …

We study efficient mechanisms for differentially private kernel density estimation (DP-KDE).
Prior work for the Gaussian kernel described algorithms that run in time exponential in the …

In this paper, we give an almost linear time and space algorithms to sample from an
exponential mechanism with an $\ell_1 $-score function defined over an exponentially large …

Robust Gray codes were introduced by (Lolck and Pagh, SODA 2024). Informally, a robust
Gray code is a (binary) Gray code G so that, given a noisy version of the encoding G (j) of an …

Integer data is typically made differentially private by adding noise from a Discrete Laplace
(or Discrete Gaussian) distribution. We study the setting where differential privacy of a …

A robust Gray code, formally introduced by (Lolck and Pagh, SODA 2024), is a Gray code
that additionally has the property that, given a noisy version of the encoding of an integer j, it …

Computing the quantile of a massive data stream has been a crucial task in networking and
data management. However, existing solutions assume a centralized model where one data …

To ensure differential privacy, one can reveal an integer fuzzily in two ways:(a) add some
Laplace noise to the integer, or (b) encode the integer as a binary string and add iid BSC …