calibrateAnalyticGaussianMechanism: Calibrate Analytic Gaussian Mechanism

In DPpack: Differentially Private Statistical Analysis and Machine Learning

Calibrate Analytic Gaussian Mechanism

Description

Calibrate a Gaussian perturbation for differential privacy using the analytic Gaussian mechanism \insertCiteBalle2018DPpack.

Usage

calibrateAnalyticGaussianMechanism(epsilon, delta, sensitivity, tol = 1e-12)

Arguments

epsilon	Positive real number defining the epsilon privacy parameter.
delta	Positive real number defining the delta privacy parameter.
sensitivity	Real number corresponding to the l2-global sensitivity.
tol	Error tolerance for binary search.

Value

Standard deviation of Gaussian noise needed to achieve (epsilon, delta)-DP for given global sensitivity.

References

\insertRef

Balle2018DPpack

DPpack documentation built on Sept. 30, 2024, 9:41 a.m.