P-COQS builds differentially private conformal prediction sets by replacing the calibration quantile with a binary-search privatized quantile, yielding approximate coverage with a computable error bound.
The numbers are metric averages over 1000 runs (per method and α value) and in parentheses is the corresponding variance of the metrics
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Differentially Private Conformal Prediction via Quantile Binary Search
P-COQS builds differentially private conformal prediction sets by replacing the calibration quantile with a binary-search privatized quantile, yielding approximate coverage with a computable error bound.