activity
20052026
most citedScalable and Differentially Private Distributed Aggregation in the Shuffled Model

65 citations · 417 across the 62 of their papers we have counts for

collaborators

78 papers

cs.DS2026

Stuffed IBLTs: Optimal Linear Multiset Sketches

Jonas Klausen, Rasmus Pagh, Stefan Walzer

A \emph{linear sketch} is a randomized linear mapping of a vector to a lower dimensional sketch vector, designed to preserve relevant information about . We consider sketche…

cs.DS2025★ 2 cited

Modern Minimal Perfect Hashing: A Survey

Hans-Peter Lehmann, Thomas Mueller, Rasmus Pagh +4

Given a set of keys, a perfect hash function for maps the keys in to the first integers without collisions. It may return an arbitrary result for any key…

cs.DS2025

Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation

Ninh Pham, Rasmus Pagh

Approximation of non-linear kernels using random feature maps has become a powerful technique for scaling kernel methods to large datasets. We propose , an…

cs.DS2023

Optimal Non-Adaptive Cell Probe Dictionaries and Hashing

Kasper Green Larsen, Rasmus Pagh, Giuseppe Persiano +3

We present a simple and provably optimal non-adaptive cell probe data structure for the static dictionary problem. Our data structure supports storing a set of n key-value pairs fr…

cs.CR2023

Differentially Private Selection from Secure Distributed Computing

Ivan Damgård, Hannah Keller, Boel Nelson +2

Given a collection of vectors , the selection problem asks to report the index of an "approximately largest" entry in .…

cs.LG2023★ 2 cited

A Smooth Binary Mechanism for Efficient Private Continual Observation

Joel Daniel Andersson, Rasmus Pagh

In privacy under continual observation we study how to release differentially private estimates based on a dataset that evolves over time. The problem of releasing private prefix s…