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

DevFeed: [Project Lighthouse -- Part 3: Introducing project-lighthouse-anonymize](<https://devfeed.tech/articles/project-lighthouse-part-3-introducing-project-lighthouse-anonymize-1220.md>)

Original publisher: [Read original article](<https://medium.com/airbnb-engineering/project-lighthouse-part-3-introducing-project-lighthouse-anonymize-74f8b26653fb?source=rss----53c7c27702d5---4>)

Author: Adam Bloomston

Published: 2026-08-25T17:01:02Z

Content type: article

Language: en

Sources: [The Airbnb Tech Blog - Medium](<https://devfeed.tech/sources/the-airbnb-tech-blog-medium.md>)

Topics: [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Python](<https://devfeed.tech/topics/python.md>), [Data Quality](<https://devfeed.tech/topics/data-quality.md>), [Code](<https://devfeed.tech/topics/code.md>), [Programming](<https://devfeed.tech/topics/programming.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [privacy](<https://devfeed.tech/tags/privacy.md>), [python](<https://devfeed.tech/tags/python.md>), [scale](<https://devfeed.tech/tags/scale.md>), [technology](<https://devfeed.tech/tags/technology.md>), [validation](<https://devfeed.tech/tags/validation.md>)

## AI overview

Project Lighthouse -- Part 3 introduces project-lighthouse-anonymize, an open-source Python library for privacy-preserving anonymization. The article describes the project's k-anonymity methodology, scalable Core Mondrian algorithm, parallel processing architecture, and data quality validation framework, alongside two new technical papers.

## Source excerpt

Project Lighthouse -- Part 3: Introducing project-lighthouse-anonymizeThe data in Project Lighthouse is powered by privacy-preserving anonymization code. We've put this code into open source, and published two new technical papers detailing the scalable algorithms and data quality frameworks behind it. By: Adam Bloomston Introduction In 2020, we launched Project Lighthouse, which we developed in partnership with leading civil rights and privacy organizations. As our 2020 announcement details, Project Lighthouse enables us to measure potential disparities in user experiences. This work uses perceived race data that is never linked to individual accounts; we only use this data for measuring potential disparities, and users who want to opt-out can do so by turning off the data use settings in their account's Privacy page. Our results, shared in 2024, demonstrate how we use these analyses to measure our progress in mitigating those disparities. Earlier this year, we open-sourced project-lighthouse-anonymize, the Python library that powers Project Lighthouse's anonymization process. To provide the full technical foundation for this work, we also published two new papers on arXiv alongside the code release. Together with our original 2020 paper, these three papers form a complete story: the foundational methodology, the scalable implementation, and the quality validation framework. The foundational methodology (2020) Our original 2020 paper established the privacy-by-design approach for Project Lighthouse and provides the rationale for choosing k-anonymity as the technical privacy model to prevent sensitive attribute disclosure at scale. For an introduction to this paper, see our first blog post on p-sensitive k-anonymity and our second blog post on measurement with anonymized data. Core Mondrian: Scalable partition-based anonymization (2025) The first of our new papers, Core Mondrian: Basic Mondrian beyond k-anonymity, presents the k-anonymity algorithm at the heart of th