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autodp: A flexible and easy-to-use package for differential privacy

License: Apache License 2.0

Python 19.16% Jupyter Notebook 80.84%

autodp's Introduction

autodp: Automating differential privacy computation

Highlights:

  1. An RDP (Renyi Differential Privacy) based analytical Moment Accountant implementation that is numerically stable.
  2. Supports privacy amplification for generic RDP algorithm for subsampling without replacement and poisson sampling.
  3. Stronger composition than the optimal composition using only (ε,δ)-DP.
  4. A privacy calibrator that numerically calibrates noise to privacy requirements using RDP.
  5. Bring Your Own Mechanism: Just implement the RDP of your own DP algorithm as a function.

How to use?

It's easy. Just run:

pip install autodp

Then follow the Jupyter notebooks in the tutorials folder to get started.

Notes:

  • pip should automatically install all the dependences for you.
  • Currently we support only Python3.
  • You might need to run pip3 install autodp --upgrade

Research Papers:

Examples:

Composing Subsampled Gaussian Mechanisms (high noise)Composing Subsampled Gaussian Mechanisms (low noise)

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.

Composing Subsampled Laplace Mechanisms (high noise)Composing Subsampled Laplace Mechanisms (low noise)

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.

How to Contribute?

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)! Please explain clearly what the contribution is about and attach/cite papers whenever appropriate.

autodp's People

Contributors

yuxiangw avatar jeremy43 avatar

Watchers

James Cloos avatar paper2code - bot avatar

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