tff.analytics.differential_privacy.analytic_gauss_stddev
Compute the stddev for the Gaussian mechanism with the given DP params.
tff.analytics.differential_privacy.analytic_gauss_stddev(
epsilon, delta, norm_bound, tol=1e-12
)
Calibrate a Gaussian perturbation for differential privacy using the
analytic Gaussian mechanism of [Balle and Wang, ICML'18].
Reference: http://proceedings.mlr.press/v80/balle18a/balle18a.pdf
Arguments |
epsilon
|
Target epsilon (0 < epsilon <= 500). The epsilon value is limited
to at most 500 in this implementation because large epsilons causes
overflow in python.
|
delta
|
Target delta (0 < delta < 1).
|
norm_bound
|
Upper bound on L2 global sensitivity (norm_bound >= 0).
|
tol
|
Error tolerance for binary search (tol > 0).
|
Returns |
sigma
|
Standard deviation of Gaussian noise needed to achieve
(epsilon,delta)-DP under the given norm_bound.
|
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Last updated 2024-09-20 UTC.
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