Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy

ArXi:2603.07522v1 Announce Type: cross Privacy protection and uncertainty quantification are increasingly important in data-driven decision making. Conformal prediction provides finite-sample marginal coverage, but existing private approaches often rely on data splitting, reducing the effective sample size. We propose a full-data privacy-preserving conformal prediction framework that avoids splitting.