Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data via training data reconstruction attacks, a major vulnerability of modern machine learning methods. This paper makes three primary contributions: establishing a mathematical framework to express the problem, characterising the features of the training data that are vulnerable via a maximum mean discrepancy equivalance and outlining a score matching framework for reconstructing data in both Bayesian and non-Bayesian models, the former is a first in the literature.
@article{arxiv.2507.18372,
title = {On Reconstructing Training Data From Bayesian Posteriors and Trained Models},
author = {George Wynne},
journal= {arXiv preprint arXiv:2507.18372},
year = {2025}
}