calibrateAnalyticGaussianMechanism {DPpack} | R Documentation |
Calibrate a Gaussian perturbation for differential privacy using the analytic Gaussian mechanism (Balle and Wang 2018).
calibrateAnalyticGaussianMechanism(epsilon, delta, sensitivity, tol = 1e-12)
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. |
Standard deviation of Gaussian noise needed to achieve
(epsilon, delta)
-DP for given global sensitivity.
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.