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Tumult Core

Tumult Core is a programming framework for implementing differentially private algorithms.

The design of Tumult Core is based on the design proposed in the OpenDP White Paper, and can automatically verify the privacy properties of algorithms constructed from Tumult Core components. Tumult Core is scalable, includes a wide variety of components, and supports multiple privacy definitions.

Note

This software is part of the OpenDP Commons. As such, the OpenDP Executive Committee commits to:

  • Releasing this software under an OSI approved license, in this case the Apache License.
  • Ensuring there are at least two maintainers, in this case Tom Magerlein (tmager), Daniel Simmons-Marengo (Maegereg), and Damien Desfontaines (TedTed), who will respond within a month to new issues and PRs.
  • Only making changes on main through PRs, and getting approval on these PRs before merging.
  • On an annual basis, recruiting one or more volunteers (not active contributors) who will conduct a health-check, focused not on the details of the algorithms but on the health of this repo as open source software. Their report will be linked here. The next (and first) health-check is scheduled for June 2027.

Installation

See the installation instructions in the documentation for information about setting up prerequisites such as Spark and Java.

Once the prerequisites are installed, you can install Tumult Core using pip.

pip install tmlt.core

Documentation

The full documentation is located at https://docs.tmlt.dev/core/latest.

Support

If you have any questions/concerns, please create an issue or reach out to us on Slack.

Contributing

We welcome external volunteers! If you are interested in contributing, please let us know on Slack. See CONTRIBUTING.md for information.

License

Tumult Core's source code is licensed under the Apache License, version 2.0 (Apache-2.0). Tumult Core's documentation is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA-4.0).

About

Tumult Core is a collection of composable components for implementing algorithms to perform differentially private computations.

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