Bounded-load calibration splits for differentially private graph conformal prediction
It is shown that private graph calibration can be framed as a structural problem: choosing a calibration set that is large enough to provide useful calibration, yet sparse enough that the privacy noise remains manageable, and it is formalized that it is NP-hard.
Patrick Indri
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