Sparse discrete-Laplace and Gaussian local DP mechanisms admit exact privacy characterizations, with support cardinality as the key parameter that sets a minimum size for nontrivial approximate privacy and yields an optimal smallest-support design rule.
Randomized response: A survey technique for eliminating evasive answer bias
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Sparse Discrete Laplace and Gaussian Mechanisms under Local Differential Privacy
Sparse discrete-Laplace and Gaussian local DP mechanisms admit exact privacy characterizations, with support cardinality as the key parameter that sets a minimum size for nontrivial approximate privacy and yields an optimal smallest-support design rule.
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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.