Audit finds DP violations in 5 of 9 mechanisms in Apple's framework due to insecure floating-point samplers and disabled local DP in secure aggregation, impacting 87% of macOS Sonoma and 68% of Sequoia data collection.
A statistical framework for differential privacy
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Exact composition of mechanisms under multiple simultaneous DP constraints is represented as a mixture of heterogeneous compositions using a structural lemma for binary hypothesis tests.
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.
At typical differential privacy levels, Cox models lose significance for about 90% of covariates and drop to random predictive performance, with usable results requiring much weaker privacy.
citing papers explorer
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Auditing Apple's DifferentialPrivacy.framework: Implementation Bugs, Misconfigurations, and Practical Risks
Audit finds DP violations in 5 of 9 mechanisms in Apple's framework due to insecure floating-point samplers and disabled local DP in secure aggregation, impacting 87% of macOS Sonoma and 68% of Sequoia data collection.
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Composition Theorems for Multiple Differential Privacy Constraints
Exact composition of mechanisms under multiple simultaneous DP constraints is represented as a mixture of heterogeneous compositions using a structural lemma for binary hypothesis tests.
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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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Benchmarking the Utility of Privacy-Preserving Cox Regression Under Data-Driven Clipping Bounds: A Multi-Dataset Simulation Study
At typical differential privacy levels, Cox models lose significance for about 90% of covariates and drop to random predictive performance, with usable results requiring much weaker privacy.