autodp_core.py
mechanism_zoo
, transformer_zoo
, calibrator_zoo
. The new API makes it extremely easy to obtain state-of-the-art privacy guarantees for your favorite randomized mechanisms, with just a few lines of codes.
It's easy. Just run:
pip install autodp
or
pip3 install autodp
Check out the Jupyter notebooks in the tutorials
folder to get started.
pip
should automatically install all the dependences for you.pip install autodp --upgrade
Install it locally by:
pip install -e .
Follow the standard practice. Fork the repo, create a branch, develop the edit and send a pull request. One of the maintainers are going to review the code and merge the PR. Alternatively, please feel free to creat issues to report bugs, provide comments and suggest new features.
At the moment, contributions to examples, tutorials, as well as the RDP of currently unsupported mechanisms are most welcome (add them to RDP_bank.py
)!
Also, you may add new mechanisms to mechanism_zoo.py
. Contributions to transformer_zoo.py
and calibrator_zoo.py
are trickier, please email us!
Please explain clearly what the contribution is about in the PR and attach/cite papers whenever appropriate.
Figure 1: Composing subsampled Gaussian Mechanisms. Left: High noise setting with σ=5, γ=0.001, δ=1e-8. Right: Low noise setting with σ=0.5, γ=0.001, δ=1e-8.
Figure 2: Composing subsampled Laplace Mechanisms. Left: High noise setting with b=2, γ=0.001, δ=1e-8. Right: Low noise setting with b=0.5, γ=0.001, δ=1e-8.