privacy-estimates 0.1.0.post1

Empirical Privacy Estimates

Empirical Estimation of Differential Privacy

This repository provides utilities for estimating DP-$\varepsilon$ from the confusion matrix of a membership inference attack based on the paper Bayesian Estimation of Differential Privacy.

Installation

Simply run the following command to install the privacy-estimates python package. It should install all the relevant dependencies as well.

pip install privacy-estimates

Example

The following command takes the output of a membership inference attack on a target model or multiples models in the form of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). It also requires the value for $\alpha$ which states the significance level of the estimate for two sided intervals of the estimated $\varepsilon$ value.

For example, we can post-proces the attack outputs of a CNN trained on CIFAR10 with $(\varepsilon = 10, \delta = 10^{-5})$ by running

python scripts/estimate-epsilon.py --alpha 0.1 --delta 1e-5 --TP 487 --TN 1 --FP 512 --FN 0 

This should take approximately 5 minutes and produce the following output

Method             Interval                Significance level  eps_lo  eps_hi
Joint beta (ours)  two-sided equal-tailed  0.100               0.145   6.399
Joint beta (ours)  one-sided               0.050               0.145   inf
Clopper Pearson    two-sided equal-tailed  0.100               0.000   inf
Clopper Pearson    one-sided               0.050               0.000   inf
Jeffreys           two-sided equal-tailed  0.100               0.000   inf
Jeffreys           one-sided               0.050               0.000   inf

Tests

We provide a few test cases which can be run by

pytest .

Contributing

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