README.md

(This Repository is no longer under development. R-bindings are planned for the near-future, under another repository)

Please note that we are renaming the toolkit and will be introducing the new name in the coming weeks.

Core Differential Privacy Library R Bindings

The R bindings are a sub-project of Core repository. See also the accompanying System repository and Samples repository for this system.

IMPORTANT

The R language bindings are not yet minimally functional.

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 R Bindings

Components

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

Architecture

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 in R 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

From Source

  1. Clone the repository

    git clone $REPOSITORY_URI --recurse-submodules
    
  2. Install the Core library dependencies https://github.com/opendifferentialprivacy/smartnoise-core#installation

  3. Install the R bindings

    R
    source("scripts/develop.R")
    install_package()
    build_doc()
    

Documentation

(In process.)

Communication

(In process.)

Core Rust Documentation

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.



opendifferentialprivacy/whitenoise-core-R documentation built on Oct. 28, 2020, 8:04 a.m.