The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
In today's data-driven world, more and more researchers and data scientists use machine learning to create better models or more innovative solutions for a better future.
These models often tend to handle sensitive or personal data, which can cause privacy issues. For example, some AI models can memorize details about the data they've been trained on and could potentially leak these details later on.
To help measure sensitive data leakage and reduce the possibility of it happening, there is a mathematical framework called differential privacy.
In 2020, OpenMined created a Python wrapper for Google's Differential Privacy project called PyDP. The library provides a set of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information. Therefore, with PyDP you can control the privacy guarantee and accuracy of your model written in Python.
Things to remember about PyDP:
To install PyDP, use the PyPI package manager:
pip install python-dp
(If you have
pip3separately for Python 3.x, use
pip3 install python-dp.)
Refer to the curated list of tutorials and sample code to learn more about the PyDP library.
Example: calculate the Bounded Mean
# Import PyDP import pydp as dp # Import the Bounded Mean algorithm from pydp.algorithms.laplacian import BoundedMean
Calculate the Bounded Mean
BoundedMean(epsilon: double, lower: int, upper: int)
epsilon: a Double, between 0 and 1, denoting the privacy threshold,
measures the acceptable loss of privacy (with 0 meaning no loss is acceptable)
upper: Integers, representing lower and upper bounds, respectively
x = BoundedMean(0.6, 1, 10)
If the lower and upper bounds are not specified,
PyDP automatically calculates these bounds
x = BoundedMean(epsilon: double)
x = BoundedMean(0.6)
Calculate the result
Currently supported data types are integers and floats
Future versions will support additional data types
Go to resources to learn more about differential privacy.
If you have questions about the PyDP library, join OpenMined's Slack and check the #lib_pydp channel. To follow the code source changes, join #codedppython.
To contribute to the PyDP project, read the guidelines.
Pull requests are welcome. If you want to introduce major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.