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.
AUTHORS
Written by
Akash Bharadwaj
Graham Cormode
Publisher
PRIVACY IN MACHINE LEARNING NeurIPS 2021 Workshop
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