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a pluggable library of differentially private algorithms and mechanisms for releasing privacy preserving queries and statistics

Project description

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WhiteNoise Core Differential Privacy Library Python Bindings

The python bindings are a sub-project of Whitenoise-Core. See also the accompanying WhiteNoise-System and WhiteNoise-Samples repositories for this system.

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

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

Binaries

  • (forthcoming PyPi binaries via milksnake)

From Source

  1. Clone the repository

     git clone $REPOSITORY_URI --recurse-submodules
    
  2. Install Whitenoise-core dependencies
    https://github.com/opendifferentialprivacy/whitenoise-core#installation

  3. Generate code

     python3 scripts/code_generation.py
    
  4. Install the python bindings

     pip install -e ".[test,plotting]"
    

    I recommend using scripts/debug_*.sh if you are developing the package.


Documentation

ReadTheDocs documentation.

Communication

(In process.)

Releases and Contributing

Please let us know if you encounter a bug by creating an issue.

We appreciate all contributions. We 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 PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.

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