Tabular diffusion models leak membership information via attacks even with partial attacker knowledge, and common heuristic privacy metrics like distance-to-closest-record are unreliable.
That which we call private
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Chernoff DP is sandwiched between KL DP and ε-DP, outperforms KL in numerical Laplace-mechanism tests, and yields a new upper bound on adversary membership advantage compared with (ε,δ)-DP bounds.
citing papers explorer
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On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics
Tabular diffusion models leak membership information via attacks even with partial attacker knowledge, and common heuristic privacy metrics like distance-to-closest-record are unreliable.
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Chernoff Information as a Privacy Constraint for Adversarial Classification and Membership Advantage
Chernoff DP is sandwiched between KL DP and ε-DP, outperforms KL in numerical Laplace-mechanism tests, and yields a new upper bound on adversary membership advantage compared with (ε,δ)-DP bounds.