Computation-Utility-Privacy Tradeoffs in Bayesian Estimation

ArXi:2603.18254v1 Announce Type: cross Bayesian methods lie at the heart of modern data science and provide a powerful scaffolding for estimation in data-constrained settings and principled quantification and propagation of uncertainty. Yet in many real-world use cases where these methods are deployed, there is a natural need to preserve the privacy of the individuals whose data is being scrutinized.