Gespeichert in:
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.18372 |
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| _version_ | 1866916861643325440 |
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| author | Wynne, George |
| author_facet | Wynne, George |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18372 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Reconstructing Training Data From Bayesian Posteriors and Trained Models Wynne, George Machine Learning Statistics Theory 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. |
| title | On Reconstructing Training Data From Bayesian Posteriors and Trained Models |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2507.18372 |