Poster
in
Workshop: Privacy in Machine Learning (PriML) 2021
Sample-and-threshold differential privacy: Histograms and applications
Graham Cormode
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.
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