Skip to main content

OpenDP

Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. License: MIT Python ci tests

The OpenDP Library is a modular collection of statistical algorithms that adhere to the definition of differential privacy. It can be used to build applications of privacy-preserving computations, using a number of different models of privacy. OpenDP is implemented in Rust, with bindings for easy use from Python.

The architecture of the OpenDP Library is based on a conceptual framework for expressing privacy-aware computations. This framework is described in the paper A Programming Framework for OpenDP.

The OpenDP Library is part of the larger OpenDP Project, a community effort to build trustworthy, open source software tools for analysis of private data. (For simplicity in these docs, when we refer to “OpenDP,” we mean just the library, not the entire project.)

Status

OpenDP is under development, and we expect to release new versions frequently, incorporating feedback and code contributions from the OpenDP Community. It's a work in progress, but it can already be used to build some applications and to prototype contributions that will expand its functionality. We welcome you to try it and look forward to feedback on the library! However, please be aware of the following limitations:

OpenDP, like all real-world software, has both known and unknown issues. If you intend to use OpenDP for a privacy-critical application, you should evaluate the impact of these issues on your use case.

More details can be found in the Limitations section of the User Guide.

Installation

The easiest way to install OpenDP is using pip (the package installer for Python):

$ pip install opendp

More information can be found in the Getting Started section of the User Guide.

Documentation

The full documentation for OpenDP is located at https://docs.opendp.org. Here are some helpful entry points:

Getting Help

If you're having problems using OpenDP, or want to submit feedback, please reach out! Here are some ways to contact us:

Contributing

OpenDP is a community effort, and we welcome your contributions to its development! If you'd like to participate, please see the Contributing section of the Developer Guide

Metadata

Release files for opendp 0.2.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for opendp 0.2.4
File Interpreter ABI Platform
opendp-0.2.4-py3-none-any.whl Python 3 none any Details

Release files / opendp-0.2.4-py3-none-any.whl

Download URL opendp-0.2.4-py3-none-any.whl
Size 21.5 MB
Tags Python 3
SHA-256 checksum
How to use checksums
fa961826c8b0dc36a9be788cb2d14c98c07db30f430cc72d5ae116ae8be280da
BLAKE2b-256 checksum
How to use checksums
bd02c352e06ac652622d34882a95f18400d7d80ae16b195d2eb7360f0db1536c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.15
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page