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The paper proposes a lightweight defense framework to enhance the empirical privacy of DP-SGD without theoretical privacy cost, validated through extensive audits across models, datasets, and threat models.
This paper proposes an LLM-as-Discriminator method to audit privacy of synthetic tabular data by asking an LLM to classify samples as real or synthetic, showing that LLM discrimination can serve as a practical privacy audit signal.