Privacy-preserving data anonymization using k-anonymity and related algorithms
Project description
Project Lighthouse Anonymize
Privacy-preserving data anonymization using k-anonymity and related algorithms.
Installation
pip install project-lighthouse-anonymize
Documentation
Full documentation: https://project-lighthouse-anonymize.readthedocs.io
Publications
This work builds on research into privacy-preserving data analysis:
- Measuring Discrepancies in Airbnb Guest Acceptance Rates Using Anonymized Demographic Data - The foundational paper for Project Lighthouse
- Core Mondrian: Scalable Mondrian for Partition-Based Anonymization - Covers the anonymization algorithm
- Measuring data quality for Project Lighthouse - Covers the way we measure the impact of anonymization
Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
Releasing
To create a new release, use the /release command in Claude Code. This will:
- Analyze changes since the last release
- Propose an appropriate version bump following semantic versioning
- Update CHANGELOG.md
- Create and push a git tag
- Trigger automated PyPI publishing via GitHub Actions
License
This project is licensed under the MIT License - see LICENSE file for details.
Acknowledgments
Developed by the Airbnb Anti-Discrimination & Equity team.
Citation
If you use this software in your research, please cite:
Bloomston, A., & Airbnb Anti-Discrimination & Equity Engineering Team. (2026).
Project Lighthouse Anonymize. https://github.com/airbnb/project-lighthouse-anonymize
BibTeX:
@software{bloomston2025lighthouse,
author = {Bloomston, Adam and {Airbnb Anti-Discrimination \& Equity Engineering Team}},
title = {Project Lighthouse Anonymize},
year = {2026},
url = {https://github.com/airbnb/project-lighthouse-anonymize},
license = {MIT}
}
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