TY - JOUR
T1 - Differentially Private Approximate Quantiles
AU - Kaplan, Haim
AU - Schnapp, Shachar
AU - Stemmer, Uri
N1 - Publisher Copyright:
Copyright © 2022 by the author(s)
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85163078320&partnerID=8YFLogxK
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AN - SCOPUS:85163078320
SN - 2640-3498
VL - 162
SP - 10751
EP - 10761
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
T2 - 39th International Conference on Machine Learning, ICML 2022
Y2 - 17 July 2022 through 23 July 2022
ER -