Differentially Private Approximate Quantiles

Haim Kaplan, Shachar Schnapp, Uri Stemmer

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


In this work we study the problem of differentially private (DP) quantiles, in which given dataset X and quantiles q1,..., qm ∈ [0, 1], we want to output m quantile estimations which are as close as possible to the true quantiles and preserve DP. We describe a simple recursive DP algorithm, which we call Approximate Quantiles (AQ), for this task. We give a worst case upper bound on its error, and show that its error is much lower than of previous implementations on several different datasets. Furthermore, it gets this low error while running time two orders of magnitude faster that the best previous implementation.

Original languageEnglish
Pages (from-to)10751-10761
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 1 Jan 2022
Event39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States
Duration: 17 Jul 202223 Jul 2022

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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