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Private Statistical Estimation of Many Quantiles


This work studies the estimation of many statistical quantiles under differential privacy. More precisely, given a distribution and access to i.i.d. samples from it, we study the estimation of the inverse of its cumulative distribution function (the quantile function) at specific points. For instance, this task is of key importance in private data generation. We present two different approaches. The first one consists in privately estimating the empirical quantiles of the samples and using this result as an estimator of the quantiles of the distribution. In particular, we study the statistical properties of the recently published algorithm introduced by Kaplan et al. 2022 that privately estimates the quantiles recursively. The second approach is to use techniques of density estimation in order to uniformly estimate the quantile function on an interval. In particular, we show that there is a tradeoff between the two methods. When we want to estimate many quantiles, it is better to estimate the density rather than estimating the quantile function at specific points.
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Dates and versions

hal-03986170 , version 1 (13-02-2023)
hal-03986170 , version 2 (22-05-2023)



Clément Lalanne, Aurélien Garivier, Rémi Gribonval. Private Statistical Estimation of Many Quantiles. ICML 2023 - 40th International Conference on Machine Learning, Jul 2023, Honolulu, United States. ⟨hal-03986170v2⟩
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