  In real-world settings involving consequential decision-making, the
deployment of machine learning systems generally requires both reliable
uncertainty quantification and protection of individuals' privacy. We present a
framework that treats these two desiderata jointly. Our framework is based on
conformal prediction, a methodology that augments predictive models to return
prediction sets that provide uncertainty quantification -- they provably cover
the true response with a user-specified probability, such as 90%. One might
hope that when used with privately-trained models, conformal prediction would
yield privacy guarantees for the resulting prediction sets; unfortunately this
is not the case. To remedy this key problem, we develop a method that takes any
pre-trained predictive model and outputs differentially private prediction
sets. Our method follows the general approach of split conformal prediction; we
use holdout data to calibrate the size of the prediction sets but preserve
privacy by using a privatized quantile subroutine. This subroutine compensates
for the noise introduced to preserve privacy in order to guarantee correct
coverage. We evaluate the method with experiments on the CIFAR-10, ImageNet,
and CoronaHack datasets.
