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A novel auditing framework reveals that synthetic data can leak private information without model access, challenging assumptions about data privacy in generative AI.
Surprisingly, using only a single inner loop update in data mixing can lead to failure, and the optimal number of inner loop steps scales logarithmically with the parameter update budget.