Privacy Preserving Recalibration under Domain Shift

Abstract

Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recalibration algorithms can greatly improve a model's probability estimates; however, existing algorithms are not applicable in real-world situations where the test data follows a different distribution from the training data, and privacy preservation is paramount (e.g. protecting patient records). We introduce a framework that abstracts out the properties of recalibration problems under differential privacy constraints. This framework allows us to adapt existing recalibration algorithms to satisfy differential privacy while remaining effective for domain-shift situations. Guided by our framework, we also design a novel recalibration algorithm, accuracy temperature scaling, that outperforms prior work on private datasets. In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy. On the 15 highest severity perturbations of the ImageNet-C dataset, our method achieves a median ECE of 0.029, over 2x better than the next best recalibration method and almost 5x better than without recalibration.

A Closer Look at the Calibration of Differentially Private Learners

“…Our work is not the first to study calibration under learning with DP, but we provide a more comprehensive characterization of privacy-calibration tradeoffs and solutions that improve this tradeoff which are both simpler and more effective. Luo et al [24] studied private calibration for out-of-domain settings, but did not study whether DP-SGD causes miscalibration in-domain. Angelopoulos et al [25] modified split conformal prediction to be privacy-preserving, but they only studied vision models and their private models have substantial performance decrease compared to non-private ones.…”