Sample-and-threshold differential privacy: Histograms and applicationsDownload PDF

Published: 04 Nov 2021, Last Modified: 15 May 2023PRIML 2021 PosterReaders: Everyone
Keywords: histograms, differential privacy, federated analytics
TL;DR: Sampling alone combined with a threshold is sufficient to give differential privacy for the core problem of histograms.
Abstract: Federated analytics aims to compute accurate statistics from distributed datasets. A "Differential Privacy" (DP) guarantee is usually desired by the users of the devices storing the data. In this work, we prove a strong $(\epsilon, \delta)$-DP guarantee for a highly practical sampling-based procedure to derive histograms. We also provide accuracy guarantees and show how to apply the procedure to estimate quantiles and modes.
Paper Under Submission: The paper is NOT under submission at NeurIPS
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