On Reconstructing Training Data From Bayesian Posteriors and Trained Models

• URL: http://arxiv.org/abs/2507.18372v1
• Date: Thu, 24 Jul 2025 12:49:41 GMT
• Title: On Reconstructing Training Data From Bayesian Posteriors and Trained Models
• Abstract summary: Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data.<n>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.
• Score: 4.061135251278187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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