a pluggable library of differentially private algorithms and mechanisms for releasing privacy preserving queries and statistics
Core Differential Privacy Library Python Bindings
This repository contains python bindings to the Core library and its underlying Rust binaries.
- For examples of this library in action, please see the Python notebooks in the Samples repository.
- In addition, see the accompanying System repository repository which includes tools for differential privacy.
Differential privacy is the gold standard definition of privacy protection. This project aims to connect theoretical solutions from the academic community with the practical lessons learned from real-world deployments, to make differential privacy broadly accessible to future deployments. Specifically, we provide several basic building blocks that can be used by people involved with sensitive data, with implementations based on vetted and mature differential privacy research. In the Core library, we provide a pluggable open source library of differentially private algorithms and mechanisms for releasing privacy preserving queries and statistics, as well as APIs for defining an analysis and a validator for evaluating these analyses and composing the total privacy loss on a dataset.
This library provides an easy-to-use interface for building analyses.
Differentially private computations are specified as a protobuf analysis graph that can be validated and executed to produce differentially private releases of data.
- More about the Core Python Bindings
- Core Documentation
- Releases and Contributing
More about Core Python Bindings
For a full listing of the extensive set of components available in the library see this documentation.
The Core library system architecture is described in the parent project. This package is an instance of the language bindings. The purpose of the language bindings is to provide a straightforward programming interface to Python for building and releasing analyses.
Logic for determining if a component releases differentially private data, as well as the scaling of noise, property tracking, and accuracy estimates are handled by a native rust library called the Validator. The actual execution of the components in the analysis is handled by a native Rust runtime.
Initial Linux and OS X binaries are available on pypi for Python 3.6+:
pip3 install opendp-whitenoise
The binaries have been used on OS X and Ubuntu and are in the process of additional testing.
Clone the repository
git clone --recurse-submodules email@example.com:opendifferentialprivacy/whitenoise-core-python.git
If you have already cloned the repository without the submodule
git submodule init git submodule update
Install the Core dependencies
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh xcode-select --install brew install protobuf python
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh sudo apt-get install diffutils gcc make m4 python # snap for protobuf 3, because apt comes with protobuf 2 sudo snap install protobuf --classic
Install WSL and refer to the linux instructions.
Install live-reloading developer version of package
pip3 install -r requirements/dev.txt pip3 install -e .
Generate code (rerun anytime the Core changes)
Refer to troubleshooting.md if necessary.
export WN_DEBUG=true # optional- for faster compilation and slower execution python3 scripts/code_generation.py
Build documentation (optional)
- Please use GitHub issues for bug reports, feature requests, install issues, and ideas.
- Gitter is available for general chat and online discussions.
- For other requests, please contact us at firstname.lastname@example.org.
- Note: We encourage you to use GitHub issues, especially for bugs.
Releases and Contributing
Please let us know if you encounter a bug by creating an issue.
We appreciate all contributions and welcome pull requests with bug-fixes without prior discussion.
If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us.
- Sending a pull request (PR) without discussion might end up resulting in a rejected PR, because we may be taking the core in a different direction than you might be aware of.
There is also a contributing guide for new developers.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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|Filename, size opendp_whitenoise_core-0.2.1-py3-none-any.whl (6.6 MB)||File type Wheel||Python version py3||Upload date||Hashes View|
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