Tumult's differential privacy analytics API
Project description
Tumult Analytics
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.
Demo video
Want to see Tumult Analytics in action? Check out this video introducing the interface fundamentals:
A selection of more advanced features is shown on the second part of this demo, in a separate video.
Installation
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.analytics
Documentation
The full documentation is located at https://docs.tmlt.dev/analytics/latest/.
Support
If you have any questions, feedback, or feature requests, please reach out to us on Slack.
Contributing
We do not yet have a process in place to accept external contributions, but we are open to collaboration opportunities. If you are interested in contributing, please let us know via Slack.
See CONTRIBUTING.md for information about installing our development dependencies and running tests.
Citing Tumult Analytics
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}
}
License
Copyright Tumult Labs 2023
Tumult Analytics' source code is licensed under the Apache License, version 2.0 (Apache-2.0). Tumult Analytics' documentation is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA-4.0).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for tmlt_analytics-0.9.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 882d1bf60b5c1fcd39456602275028b27e2aeaf77049fc629466e32c7f921083 |
|
MD5 | da5f71ca3b89e1b2756c67805a41babf |
|
BLAKE2b-256 | a24ed7136cbdcbcf4f2a2e593d328c8542009a1c8fc45b54690baa87692484fe |