Online Locally Differentially Private Conformal Prediction via Binary Inquiries

Qiangqiang Zhang, Chenfei Gu, Xinwei Feng, Jinhan Xie, Ting Li

Keywords: Conformal Prediction, Local Differential Privacy, Online Learning, Pinball Loss

Abstract: We propose an online conformal prediction framework under local differential privacy to address the emerging challenge of privacy-preserving uncertainty quantification in streaming data environments. Our method constructs dynamic, model-free prediction sets based on randomized binary inquiries, ensuring rigorous privacy protection without requiring access to raw data. Importantly, the proposed algorithm can be conducted in a one-pass online manner, leading to high computational efficiency and minimal storage requirements with $\mathcal{O}(1)$ space complexity, making it particularly suitable for real-time applications. The proposed framework is also broadly applicable to both regression and classification tasks, adapting flexibly to diverse predictive settings. We establish theoretical guarantees for long-run coverage at a target confidence level, ensuring statistical reliability under strict privacy constraints. Extensive empirical evaluations on both simulated and real-world datasets demonstrate that the proposed method delivers accurate, stable, and privacy-preserving predictions across a range of dynamic environments.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 16521