fourier-accountant 0.12.11

Fourier Accountant for Differential Privacy

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
International Conference on Artificial Intelligence and Statistics (2020)

API and Usage

• get_delta_R(target_eps, sigma, q, ncomp, nx, L) Computes the DP delta for the remove/add neighbouring relation of datasets.
• get_delta_S(target_eps, sigma, q, ncomp, nx, L) Computes the DP delta for the substitute neighbouring relation of datasets.
• get_epsilon_R(target_delta, sigma, q, ncomp, nx, L) Computes the DP epsilon for the remove/add neighbouring relation of datasets.
• get_epsilon_S(target_delta, sigma, q, ncomp, nx, L) Computes the DP epsilon for the substitute neighbouring relation of datasets.

Parameters

• target_eps (float): Target epsilon to compute delta for
• target_delta (float): Target delta to compute epsilon for
• sigma (float or np.ndarray): Privacy noise sigma values
• q (float or np.ndarray): Subsampling ratios, i.e., how large are batches relative to the dataset
• ncomp (int or np.ndarray with integer type): Number of compositions, i.e., how many subsequent batch operations are queried
• nx (int): Number of discretiation points
• L (float): Limit for the approximation of the privacy loss distribution integral

For parameters sigma, q and ncomp either a single scalar or an array can be passed. If a scalar is passed, the value will be re-interpreted as an array of length 1. Each function then computes the privacy values (delta or epsilon) resulting from a composition of subsampled Gaussian mechanism with following parameters:

• ncomp[0] times noise level sigma[0] and subsamplign rate q[0]
• ncomp[1] times noise level sigma[1] and subsamplign rate q[1]
• etc. for a total of np.sum(ncomp) operations.

An exception is raised if sigma, q and ncomp are found to not be of the same length.

Usage Notes

Note that the functions rely on numerical approximations, which are influenced by choice of parameters nx and L. Increasing L roughly increases the range over which the integral of the privacy loss distribution is approximated. L must be chosen large enough to cover the computed epsilon, otherwise a ValueError is raised (in get_epsilon_*). nx is the number of evaluation points in $[-L,L]$.

Usage Example

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

# computing delta for given epsilon for remove/add neighbouring relation
# with varying parameters
ncomp = np.array([500, 500])
q     = np.array([0.01, 0.01])
sigma = np.array([2.0, 1.0])
delta = fourier_accountant.get_delta_R(target_eps=1.0, sigma=sigma, q=q, ncomp=ncomp)
print(delta)
# 0.0003151995621652058