Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
Abstract
We generalize the continuous observation privacy setting from Dwork et al. '10 and Chan et al. '11 by allowing each event in a stream to be a subset of some (possibly unknown) universe of items. We design differentially private (DP) algorithms for histograms in several settings, including top- selection, with privacy loss that scales with polylog, where is the maximum length of the input stream. We present a meta-algorithm that can use existing one-shot top- DP algorithms as a subroutine to continuously release private histograms from a stream. Further, we present more practical DP algorithms for two settings: 1) continuously releasing the top- counts from a histogram over a known domain when an event can consist of an arbitrary number of items, and 2) continuously releasing histograms over an unknown domain when an event has a limited number of items.
Cite
@article{arxiv.2103.16787,
title = {Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown},
author = {Adrian Rivera Cardoso and Ryan Rogers},
journal= {arXiv preprint arXiv:2103.16787},
year = {2022}
}