3 papers
math.ST2026
Bayesian Adversarial Privacy
Cameron Bell, Timothy Johnston, Antoine Luciano +1
Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphases and aims. This work introduces a novel quantitative notion of…
math.ST2026
Persuasive Privacy
Joshua J Bon, James Bailie, Judith Rousseau +1
We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that a…
stat.CO2023
Asymptotics of approximate Bayesian computation when summary statistics converge at heterogeneous rates
Caroline Lawless, Christian P. Robert, Judith Rousseau +1
We consider the asymptotic properties of Approximate Bayesian Computation (ABC) for the realistic case of summary statistics with heterogeneous rates of convergence. We allow some…