A bilevel optimization framework with curvature-guided geodesic perturbation reduces membership inference attack success while preserving downstream classification accuracy and sample quality.
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Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
A bilevel optimization framework with curvature-guided geodesic perturbation reduces membership inference attack success while preserving downstream classification accuracy and sample quality.