1 code implementation • 23 Feb 2022 • Staal A. Vinterbo
We extend this work and provide a sufficient and necessary condition for $(\epsilon, \delta)$-differential privacy for all symmetric and log-concave noise densities.
no code implementations • 18 Dec 2020 • Staal A. Vinterbo
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 $\epsilon \in (0, 1)$.
no code implementations • NeurIPS 2013 • Kamalika Chaudhuri, Staal A. Vinterbo
Differential privacy is a cryptographically motivated definition of privacy which has gained considerable attention in the algorithms, machine-learning and data-mining communities.