Feature differential privacy is a relaxation of DP that guards selected features only, and the paper's two-batch algorithm recovers subsampling amplification and improves utility over standard DP when public features exist.
Pri- vacy amplification by subsampling: Tight analyses via couplings and divergences
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Machine Learning with Privacy for Protected Attributes
Feature differential privacy is a relaxation of DP that guards selected features only, and the paper's two-batch algorithm recovers subsampling amplification and improves utility over standard DP when public features exist.