Skip to main content

Tumult's differential privacy analytics API

Project description

PyPI - Version | Documentation - Latest | Join our Slack!

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.

🚨 Important Update: the Tumult Labs Team is Joining LinkedIn 🚨

The Tumult Labs team has joined LinkedIn! 🎉 As part of this transition, we are exploring options for the future of Tumult Analytics, including finding a new home for the project. 🏡 We greatly appreciate the community’s support and contributions. If your organization is interested in maintaining or adopting Tumult Analytics, please reach out! 📩 For now, the repository remains available, and we encourage users to continue engaging with the project. We’ll provide updates as soon as we have more to share. — The Tumult Labs Team 💙

Demo video

Want to see Tumult Analytics in action? Check out this video introducing the interface fundamentals:

Screenshot of the demo video

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 2025

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tmlt_analytics-0.20.1.tar.gz (263.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tmlt_analytics-0.20.1-py3-none-any.whl (161.9 kB view details)

Uploaded Python 3

File details

Details for the file tmlt_analytics-0.20.1.tar.gz.

File metadata

  • Download URL: tmlt_analytics-0.20.1.tar.gz
  • Upload date:
  • Size: 263.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.8.20 Linux/5.15.154+

File hashes

Hashes for tmlt_analytics-0.20.1.tar.gz
Algorithm Hash digest
SHA256 364c5c3884b8c43c8a18ffee95d3c6cb10a244a2135108b7e54f11ee390533b0
MD5 a21910e3aa09d8a6d98cda497dee62e2
BLAKE2b-256 d47d85ec2508f69dd6be31464fb50d3fe244e864692f48352ebc2afc6c3644c0

See more details on using hashes here.

File details

Details for the file tmlt_analytics-0.20.1-py3-none-any.whl.

File metadata

  • Download URL: tmlt_analytics-0.20.1-py3-none-any.whl
  • Upload date:
  • Size: 161.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.8.20 Linux/5.15.154+

File hashes

Hashes for tmlt_analytics-0.20.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b2638f53d377dfd5c2307dc301d8f278a3dd72afefc9a3771874810b6ad771ef
MD5 863e7290d60eb162fb920be66ab7ef09
BLAKE2b-256 1880375033fb64b5875ebd42ce25ecba2adc08c4c0ed13e8eecc6c477e3a31c9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page