Privacy-Preserving Conformal Prediction Under Local Differential Privacy

TL;DR

This paper introduces two methods for privacy-preserving conformal prediction under local differential privacy, enabling reliable uncertainty quantification without exposing true labels or data, suitable for sensitive applications.

Contribution

It proposes novel LDP-compatible conformal prediction techniques that maintain coverage guarantees while ensuring data and label privacy in untrusted settings.

Findings

Both methods achieve finite-sample coverage guarantees.

Robust coverage is maintained even under severe randomization.

Approaches are applicable to sensitive domains like medical imaging.

Abstract

Conformal prediction (CP) provides sets of candidate classes with a guaranteed probability of containing the true class. However, it typically relies on a calibration set with clean labels. We address privacy-sensitive scenarios where the aggregator is untrusted and can only access a perturbed version of the true labels. We propose two complementary approaches under local differential privacy (LDP). In the first approach, users do not access the model but instead provide their input features and a perturbed label using a k-ary randomized response. In the second approach, which enforces stricter privacy constraints, users add noise to their conformity score by binary search response. This method requires access to the classification model but preserves both data and label privacy. Both approaches compute the conformal threshold directly from noisy data without accessing the true labels. We prove finite-sample coverage guarantees and demonstrate robust coverage even under severe randomization. This approach unifies strong local privacy with predictive uncertainty control, making it well-suited for sensitive applications such as medical imaging or large language model queries, regardless of whether users can (or are willing to) compute their own scores.