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Fiducial Matching: Differentially Private Inference for Categorical Data
Ogonnaya Michael Romanus, Younes Boulaguiem, Roberto Molinari
https://arxiv.org/abs/2507.11762

Fiducial Matching: Differentially Private Inference for Categorical Data
The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of investigation in a differentially private (DP) setting. Indeed, in addition to the randomness due to data sampling, DP delivers another source of randomness consisting of the noise added to protect an individual's data from being disclosed to a potential attacker. As a result of this convolution of noises, in many cases…