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].

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.

Arguments