  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 ApproximateQuantiles (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.
