Abstract
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 language | English |
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Pages (from-to) | 10751-10761 |
Number of pages | 11 |
Journal | Proceedings of Machine Learning Research |
Volume | 162 |
State | Published - 1 Jan 2022 |
Event | 39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States Duration: 17 Jul 2022 → 23 Jul 2022 |
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability