On Conformal Machine Unlearning
This addresses data privacy needs for users by offering a statistically rigorous approach to machine unlearning, though it is incremental as it builds on existing conformal prediction methods.
The paper tackles the lack of statistical guarantees in machine unlearning by introducing a new definition based on conformal prediction, providing uncertainty-aware guarantees and demonstrating efficacy in removing targeted data across diverse experiments.
The increasing demand for data privacy has made machine unlearning (MU) essential for removing the influence of specific training samples from machine learning models while preserving performance on retained data. However, most existing MU methods lack rigorous statistical guarantees or rely on heuristic metrics such as accuracy. To overcome these limitations, we introduce a new definition for MU based on conformal prediction (CP), providing statistically sound, uncertainty-aware guarantees without the need for the concept of naive retraining. We formalize the proposed conformal criteria that quantify how often forgotten samples are excluded from CP sets, and propose empirical metrics to measure the effectiveness of unlearning. We further present a practical unlearning method designed to optimize these conformal metrics. Extensive experiments across diverse forgetting scenarios, datasets and models demonstrate the efficacy of our approach in removing targeted data.