Conformal-DP applies conformal transformations to create a density-aware DP mechanism on Riemannian manifolds, proving ε-DP and deriving a closed-form geodesic error bound dependent only on density ratio and independent of global curvature.
Differential privacy: A survey of results,
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A differentially private fine-tuning method that constructs a quadratic utility function to allow exact sampling from a multivariate normal distribution while providing theoretical privacy guarantees.
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Conformal-DP: A Density-Aware Mechanism for Differential Privacy over Riemannian Manifolds via Conformal Transformation
Conformal-DP applies conformal transformations to create a density-aware DP mechanism on Riemannian manifolds, proving ε-DP and deriving a closed-form geodesic error bound dependent only on density ratio and independent of global curvature.
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An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
A differentially private fine-tuning method that constructs a quadratic utility function to allow exact sampling from a multivariate normal distribution while providing theoretical privacy guarantees.