Package: python-dp
Introduction to PyDPIn 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’sDifferential
Privacyproject
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:::rocket: Features differentially private algorithms including: BoundedMean, BoundedSum, Max, Count Above, Percentile, Min, Median, etc.All the computation methods mentioned above use Laplace noise only (other noise mechanisms will be added soon! :smiley:).::fire: Currently supports Linux and macOS (Windows support coming soon :smiley:)::star: Use Python 3.6+. Support for Python 3.5 and below is deprecated.InstallationTo install PyDP, use thePiPypackage manager:pipinstallpython-dp(If you havepip3separately for Python 3.x, usepip3 installpython-dp.)ExamplesRefer to thecurated listof tutorials and sample code to learn more about the PyDP library.You can also get started withan introduction to
PyDP(a Jupyter notebook) andthe carrots demo(a Python file).Example: calculate the Bounded Mean# Import PyDPimportpydpasdp# Import the Bounded Mean algorithmfrompydp.algorithms.laplacianimportBoundedMean# Calculate the Bounded Mean# Basic Structure: `BoundedMean(epsilon: float, lower_bound: Union[int, float, None], upper_bound: Union[int, float, None])`# `epsilon`: a Double, between 0 and 1, denoting the privacy threshold,#            measures the acceptable loss of privacy (with 0 meaning no loss is acceptable)x=BoundedMean(epsilon=0.6,lower_bound=1,upper_bound=10)# If the lower and upper bounds are not specified,# PyDP automatically calculates these bounds# x = BoundedMean(epsilon: float)x=BoundedMean(0.6)# Calculate the result# Currently supported data types are integers and floats# Future versions will support additional data types# (Refer to https://github.com/OpenMined/PyDP/blob/dev/examples/carrots.py)x.quick_result(input_data:list)Learning ResourcesGo toresourcesto learn more about differential privacy.Support and Community on SlackIf you have questions about the PyDP library, joinOpenMined’s Slackand check the#lib_pydpchannel. To follow the code source changes, join#code_dp_python.ContributingTo contribute to the PyDP project, read theguidelines.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.<!– ## Contributors –>LicenseApache License 2.0