Online Differentially Private Conformal Prediction for Uncertainty Quantification
Qiangqiang Zhang, Ting Li, Xinwei Feng, Xiaodong Yan, Jinhan Xie
Abstract
Traditional conformal prediction faces significant challenges with the rise of streaming data and increasing concerns over privacy. In this paper, we introduce a novel online differentially private conformal prediction framework, designed to construct dynamic, model-free private prediction sets. Unlike existing approaches that either disregard privacy or require full access to the entire dataset, our proposed method ensures individual privacy with a one-pass algorithm, ideal for real-time, privacy-preserving decision-making. Theoretically, we establish guarantees for long-run coverage at the nominal confidence level. Moreover, we extend our method to conformal quantile regression, which is fully adaptive to heteroscedasticity. We validate the effectiveness and applicability of the proposed method through comprehensive simulations and real-world studies on the ELEC2 and PAMAP2 datasets.
BibTeX
@inproceedings{
zhang2025online,
title={Online Differentially Private Conformal Prediction for Uncertainty Quantification},
author={Qiangqiang Zhang and Ting Li and Xinwei Feng and Xiaodong Yan and Jinhan Xie},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=dmZQrojdVU}
}