Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize

Summary

Airbnb has open-sourced project-lighthouse-anonymize, a Python library powering their privacy-preserving anonymization used to measure user experience disparities. The release is accompanied by technical papers detailing Core Mondrian, an extended partition-based anonymization algorithm, alongside a framework for measuring anonymized data quality. The library enforces k-anonymity and p-sensitive k-anonymity while offering built-in data quality metrics.

Takeaways

1. 1

   Core Mondrian scales traditional Mondrian anonymization using a hybrid recursive-queue execution model, NaN-pattern pre-partitioning, and dynamic suppression budgets.

2. 2

   Data quality under anonymization is validated using three primary metrics: Pearson correlation, Revised Information Loss Metric (RILM), and Normalized Mutual Information v1 (NMIv1).

3. 3

   The open-source Python library allows developers to sequentially enforce k-anonymity and p-sensitive k-anonymity on datasets and evaluate quality thresholds programmatically.