rsindper / autodp

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

autodp: Automating differential privacy computation

Author:

Yu-Xiang Wang

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:

• Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan. (2019) "Subsampled Renyi Differential Privacy and Analytical Moments Accountant.". in AISTATS-2019 (Notable Paper Award).
• Yuqing Zhu, Yu-Xiang Wang. (2019) "Poisson Subsampled Renyi Differential Privacy". ICML-2019, to appear.

Examples：

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.