Fourier Accountant for Differential Privacy
Project description
Fourier Accountant
Python code for computing tight DP-guarantees for the subsampled Gaussian mechanism.
The method is described in:
Antti Koskela, Joonas Jälkö, Antti Honkela: Computing Tight Differential Privacy Guarantees Using FFT
https://arxiv.org/abs/1906.03049
Usage
import fourier_accountant
ncomp = 1000 # number of compositions of DP queries over minibatches
q = 0.01 # subsampling ratio of minibatch
sigma = 4.0 # noise level for each query
# computing delta for given epsilon for remove/add neighbouring relation
delta = fourier_accountant.get_delta_R(target_eps=1.0, sigma=sigma, q=q, ncomp=ncomp)
print(delta)
# 4.243484012034273e-06
# computing epsilon for given delta for substitute neighbouring relation
eps = fourier_accountant.get_epsilon_S(target_delta=1e-5, sigma=sigma, q=q, ncomp=ncomp)
print(eps)
# 1.9931200626285734
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