FLIP combines a VAE, latent diffusion, Rényi DP, and CKA alignment across protected groups to produce tabular data with substantially reduced predictability of the protected attribute.
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Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation
FLIP combines a VAE, latent diffusion, Rényi DP, and CKA alignment across protected groups to produce tabular data with substantially reduced predictability of the protected attribute.