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.
Mei Ling Fang, Devendra Singh Dhami, and Kristian Kersting
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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.