Differentially private Bayesian tests

arXiv:2401.15502v2 Announce Type: replace-cross
Abstract: Differential privacy has emerged as an significant cornerstone in the realm of scientific hypothesis testing utilizing confidential data. In reporting scientific discoveries, Bayesian tests are widely adopted since they effectively circumnavigate the key criticisms of P-values, namely, lack of interpretability and inability to quantify evidence in support of the competing hypotheses. We present a novel differentially private Bayesian hypotheses testing framework that arise naturally under a principled data generative mechanism, inherently maintaining the interpretability of …

abstract arxiv bayesian cs.cr cs.lg data differential differential privacy discoveries evidence hypothesis interpretability key privacy realm reporting scientific stat.ml support testing tests the key type values