calibrateAnalyticGaussianMechanism {DPpack}R Documentation

Calibrate Analytic Gaussian Mechanism

Description

Calibrate a Gaussian perturbation for differential privacy using the analytic Gaussian mechanism (Balle and Wang 2018).

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

Balle B, Wang Y (2018). “Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising.” In Dy J, Krause A (eds.), Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, 394–403. https://proceedings.mlr.press/v80/balle18a.html.


[Package DPpack version 0.2.2 Index]