A Bayesian decision framework with probabilistic programs computes membership posteriors for Bayesian network populations and outperforms marginal-based attacks on complex dependency structures.
arXiv preprint arXiv:2302.03098 , year=
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Proposes high-temperature synthetic canaries and auxiliary-model auditing to improve empirical privacy measurement for LLM fine-tuning and synthetic data generation.
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A Bayesian Approach to Membership Inference for Statistical Release
A Bayesian decision framework with probabilistic programs computes membership posteriors for Bayesian network populations and outperforms marginal-based attacks on complex dependency structures.
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Advancing the State-of-the-Art in Empirical Privacy Auditing
Proposes high-temperature synthetic canaries and auxiliary-model auditing to improve empirical privacy measurement for LLM fine-tuning and synthetic data generation.