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

• URL: http://arxiv.org/abs/2603.07522v1
• Date: Sun, 08 Mar 2026 08:25:16 GMT
• Title: Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy
• Abstract summary: Existing private approaches often rely on data splitting, reducing the effective sample size.<n>We propose a full-data privacy-preserving conformal prediction framework that avoids splitting.<n>We show that a generic differential privacy guarantee yields a universal coverage floor, yet cannot generally recover the nominal $1-$ level.
• Score: 5.19945480121051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Our framework leverages stability induced by differential privacy to control the gap between in-sample and out-of-sample conformal scores, and pairs this with a conservative private quantile routine designed to prevent under-coverage. We show that a generic differential privacy guarantee yields a universal coverage floor, yet cannot generally recover the nominal $1-α$ level. We then provide a refined, mechanism-specific stability analysis and yields asymptotic recovery of the nominal level. Experiments demonstrate sharper prediction sets than the split-based private baseline.

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