srayagarwal/whitenoise-core-python

Python language bindings for whitenoise-core

WhiteNoise Core
Differential Privacy Library Python Bindings

This repository contains python bindings to Whitenoise-Core and its underlying Rust binaries.

• For examples of this library in action, please see the Python notebooks in WhiteNoise-Samples.
• In addition, see the accompanying WhiteNoise-System repository which includes WhiteNoise tools for differential privacy.

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Differential privacy is the gold standard definition of privacy protection. The WhiteNoise 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 WhiteNoise Core, 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 WhiteNoise Core Python Bindings
  • Component List
  • Architecture
• Installation
  • Binaries
  • From Source
• WhiteNoise Documentation
• Communication
• Releases and Contributing

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More about WhiteNoise Core Python Bindings

Components

For a full listing of the extensive set of components available in the library see this documentation.

Architecture

The Whitenoise-core 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.

Installation

Refer to troubleshooting.md for install problems.

Binaries

Initial Linux and OS X binaries are available on pypi for Python 3.6+:

• https://pypi.org/project/opendp-whitenoise-core/
• pip3 install opendp-whitenoise-core

The binaries have been used on OS X and Ubuntu and are in the process of additional testing.

From Source

1. Clone the repository

   git clone --recurse-submodules git@github.com:opendifferentialprivacy/whitenoise-core-python.git

   If you have already cloned the repository without the submodule

   git submodule init
   git submodule update

2. Install Whitenoise-core dependencies Mac

   curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
   xcode-select --install
   brew install protobuf python

   Linux

   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

   Windows Install WSL and refer to the linux instructions.

3. Install live-reloading developer version of package

   pip3 install -r requirements/dev.txt
   pip3 install -e .

4. Generate code (rerun anytime whitenoise-core changes)

   python3 scripts/code_generation.py

5. Build documentation (optional)

   ./scripts/build_docs.sh

WhiteNoise Documentation

• Python library documentation

Communication

• 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 whitenoise@opendp.io.
  • 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.