https://github.com/microsoft/responsible-ai-toolbox-privacy
A library for statistically estimating the privacy of ML pipelines from membership inference attacks
https://github.com/microsoft/responsible-ai-toolbox-privacy
machine-learning privacy-preserving-machine-learning
Last synced: 9 months ago
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A library for statistically estimating the privacy of ML pipelines from membership inference attacks
- Host: GitHub
- URL: https://github.com/microsoft/responsible-ai-toolbox-privacy
- Owner: microsoft
- License: mit
- Created: 2022-08-03T12:36:52.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-01-24T17:28:44.000Z (over 1 year ago)
- Last Synced: 2025-01-31T16:24:33.440Z (over 1 year ago)
- Topics: machine-learning, privacy-preserving-machine-learning
- Language: Python
- Homepage:
- Size: 493 KB
- Stars: 34
- Watchers: 9
- Forks: 7
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Support: SUPPORT.md
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README
# 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.
``` bash
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
``` bash
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
``` bash
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
``` bash
pytest .
```
# Contributing
This project welcomes contributions and suggestions. Most contributions require you to
agree to a Contributor License Agreement (CLA) declaring that you have the right to,
and actually do, grant us the rights to use your contribution. For details, visit
https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need
to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the
instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.