This directory contains tools for tracking differential privacy budgets, available as part of the Google differential privacy library.
The set of DpEvent classes allow you to describe complex differentially private mechanisms such as Laplace and Gaussian, subsampling mechanisms, and their compositions. The PrivacyAccountant classes can ingest DpEvents and return the ε, δ of the composite mechanism. Privacy Loss Distributions (PLDs) and RDP accounting are currently supported.
More detailed definitions and references about PLDs can be found in our supplementary pdf document.
Our library only support Python version >= 3.9. We test this library on Linux with Python version 3.9. If you experience any problems, please file an issue on GitHub, also for other platforms or Python versions.
We provide basic examples on how to use the library in privacy_loss_distribution_basic_example.py. There are two ways to run this, either via Bazel or after installing the library using setup.py.
For running the example using Bazel, you need to have Bazel installed. Once that is done, run:
bazel build dp_accounting:all bazel run dp_accounting:privacy_loss_distribution_basic_example
For the second option, you will need the setuptools package installed. To ensure this, you may run
pip install --upgrade setuptools
Then, to demonstrate our example, run:
python setup.py install python dp_accounting/privacy_loss_distribution_basic_example.py