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Found 3 papers, 1 papers with code
Differential privacy for symmetric log-concave mechanisms
We extend this work and provide a sufficient and necessary condition for $(\epsilon, \delta)$-differential privacy for all symmetric and log-concave noise densities.
A closed form scale bound for the $(ε, δ)$-differentially private Gaussian Mechanism valid for all privacy regimes
The standard closed form lower bound on $\sigma$ for providing $(\epsilon, \delta)$-differential privacy by adding zero mean Gaussian noise with variance $\sigma^2$ is $\sigma > \Delta\sqrt {2}(\epsilon^{-1}) \sqrt {\log \left( 5/4\delta^{-1} \right)}$ for $\epsilon \in (0, 1)$.
A Stability-based Validation Procedure for Differentially Private Machine Learning
Differential privacy is a cryptographically motivated definition of privacy which has gained considerable attention in the algorithms, machine-learning and data-mining communities.
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