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Poster
Differentially Private Approximate Quantiles
Haim Kaplan · Shachar Schnapp · Uri Stemmer

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #904
in Poster Session 1 »
In this work we study the problem of differentially private (DP) quantiles, in which given dataset $X$ and quantiles $q_1, ..., q_m \in [0,1]$, 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.
Keywords: SA: Privacy-preserving Statistics and Machine Learning

Author Information

Haim Kaplan (TAU, GOOGLE)
Shachar Schnapp (Ben-Gurion University of the Negev)
Uri Stemmer (Tel Aviv University and Google Research)

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