gdpnum 0.1.0

Numeric computation of the privacy parameter in Gaussian Differential Privacy

gdpnum

Repository for numerically computing the privacy parameter in Gaussian Differential Privacy.

Quickstart

You can install the library with pip:

pip install gdpnum

DP-SGD

To analyze DP-SGD, we have:

import gdpnum

mu, regret = gdpnum.dpsgd.get_mu_and_regret_for_dpsgd(
    noise_multiplier=9.4,
    sample_rate=0.328,
    num_steps=2000
)
# (1.5685621993129137, 0.0010208130697719753)

We get the numerically computed GDP mu parameter, and regret which shows the goodness-of-fit of the GDP.

The library also includes an Opacus-compatible accountant interface:

import gdpnum

acct = gdpnum.dpsgd.CTDAccountant()
acct.step(noise_multiplier=9.4, sample_rate=0.328)
acct.get_mu_and_regret()

General mechanisms

For general mechanisms, the library relies on the privacy loss distribution objects from the dp_accounting package:

import gdpnum

pld = ...

converter = gdpnum.PLDConverter(pld)
mu, regret = converter.get_mu_and_regret()

See an example for the US Census TopDown mechanism in the notebooks folder.