RESEARCH

Sample-and-threshold differential privacy: Histograms and applications

December 15, 2021

Abstract

Federated analytics relies on the collection of accurate statistics about distributed users with a suitable guarantee. In this paper, we show how a strong (epsilon, delta)-privacy guarantee can be achieved for the fundamental problem of histogram generation in a federated setting, via a highly practical sampling-based procedure. Given such histograms, related problems such as heavy hitters and quantiles can be answered with provable error and privacy guarantees.

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AUTHORS

Written by

Akash Bharadwaj

Graham Cormode

Publisher

PRIVACY IN MACHINE LEARNING NeurIPS 2021 Workshop

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