Tumult Analytics is a library that allows users to execute differentially private operations on data without having to worry about the privacy implementation, which is handled automatically by the API. It is built atop the Tumult Core library.
See the installation instructions in the documentation for information about setting up prerequisites such as Spark.
Once the prerequisites are installed, you can install Tumult Analytics using pip.
pip install tmlt.analyticsThe full documentation is located at https://docs.tmlt.dev/analytics/latest/.
If you have any questions/concerns, please create an issue or reach out to us on Slack.
We are not yet accepting external contributions, but please let us know if you are interested in contributing via Slack.
See CONTRIBUTING.md for information about installing our development dependencies and running tests.
If you use Tumult Analytics for a scientific publication, we would appreciate citations to the published software or/and its whitepaper. Both citations can be found below; for the software citation, please replace the version with the version you are using.
@software{tumultanalyticssoftware,
author = {Tumult Labs},
title = {Tumult {{Analytics}}},
month = dec,
year = 2022,
version = {latest},
url = {https://tmlt.dev}
}
@article{tumultanalyticswhitepaper,
title={Tumult {{Analytics}}: a robust, easy-to-use, scalable, and expressive framework for differential privacy},
author={Berghel, Skye and Bohannon, Philip and Desfontaines, Damien and Estes, Charles and Haney, Sam and Hartman, Luke and Hay, Michael and Machanavajjhala, Ashwin and Magerlein, Tom and Miklau, Gerome and Pai, Amritha and Sexton, William and Shrestha, Ruchit},
journal={arXiv preprint arXiv:2212.04133},
month = dec,
year={2022}
}
Copyright Tumult Labs 2023
The Tumult Platform source code is licensed under the Apache License, version 2.0 (Apache-2.0). The Tumult Platform documentation is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA-4.0).