OpenDP Roadmap


Throughout the year, we’ll identify high-level steps that focus on OpenDP’s core initiatives and are detailed in the Roadmap below.

The big things the development team and the community are working on right now are shown in the Implementation section. In the Planning/Design section, you can see those strategic items that we've prioritized and are designing and testing with the community. In the Future section, you'll see the things that we'd like to work on but haven't yet prioritized.

This Roadmap is focused on high-level initiatives. We're always working on smaller bug fixes and enhancements. For a view of everything that the OpenDP Project Team and Community are working on right now, check out the Project Board on GitHub .



Q2/Q3 2021 - Development and Initial Release of the OpenDP Library

Q3 2021 - Documentation/Onboarding Website

Q3 2021 - OpenDP Commons Contribution Process and Guidelines

  • Establish and document the intake and vetting process of community contribution of algorithms into the OpenDP library and tools and packages that use the OpenDP library.

Q3 2021 - 2nd OpenDP Community Meeting

  • Coordinated with the release of the OpenDP library, the 2nd OpenDP community meeting will include the introduction of the newly released OpenDP Library and inviting community contribution of tools and systems to the OpenDP Commons.

Q3/Q4 2021 - Development and Initial Release of the DP Creator web application with Dataverse integration.

  • The DP Creator is a web-based application to budget workloads of statistical queries for public release.
  • Integration with Dataverse repositories will allow researchers with knowledge of their datasets to calculate DP statistics without requiring expert knowledge in programming or differential privacy.
  • GitHub: opendp/dpcreator repository | Issues
  • To learn more about the application, email us.

Q4 2021/Q1 2022 - Epsilon Registry - Initial Survey Tool

  • Create a survey tool to collect data on real-world DP use cases, including information on data domain specific problems and legal requirements, selection of privacy-loss parameters, and use of differentially private releases.
  • Based on the work of Cynthia Dwork, Nitin Kohil, and Deirdre Mulligan’s publication Differential Privacy in Practice: Expose your Epsilons!

2021 - Prototype of Federated Machine Learning 

  • Differentially private algorithms separately computed on datasets at remote locations and aggregated through postprocessing.
  • GitHub: opendp/sotto-voce
  • Contact us to learn more about this project.


Planning / Design

2021 - Integrate Federated Machine Learning with the OpenDP Library

  • Design for federation incorporated into the privacy framework, and exterior computations such as by PyTorch.
  • GitHub: opendp/sotto-voce

2021/22 - DP Explorer Application

  • Visual exploration tool for intuitively understanding DP releases created using the OpenDP library, this includes releases generated with DP Creator.

2021/22 - DP Creator in Analyst Mode

  • Design and user focused studies of workflow for allowing data scientists and archivists to create differentially private releases from a dataset by intuitively allocating their budget across a workload of desired statistics.


OpenDP Library: Advanced Composition


OpenDP Library: SQL Support


Unbiased Privacy