EXACT PRIVACY ANALYSIS OF THE GAUSSIAN SPARSE HISTOGRAM MECHANISM

Brian Karrer, Daniel Kifer, Arjun Wilkins, Danfeng Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms or large group-by queries, and more generally, releasing a set of statistics with sufficient item counts. We consider the Gaussian version of the sparse histogram mechanism and study the exact ϵ, δ differential privacy guarantees satisfied by this mechanism. We compare these exact ϵ, δ parameters to the simpler overestimates used in prior work to quantify the impact of looser privacy bounds.

Original languageEnglish (US)
JournalJournal of Privacy and Confidentiality
Volume14
Issue number1
DOIs
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Statistics and Probability
  • Computer Science Applications

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