Private Prediction Sets

• URL: http://arxiv.org/abs/2102.06202v3
• Date: Sun, 3 Mar 2024 06:47:19 GMT
• Title: Private Prediction Sets
• Authors: Anastasios N. Angelopoulos and Stephen Bates and Tijana Zrnic and Michael I. Jordan
• Abstract summary: Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
• Score: 72.75711776601973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 on large-scale computer vision datasets.

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