Privacy-Preserving Data Anonymization

This post introduces project-lighthouse-anonymize, an open-sourced Python library for privacy-preserving data anonymization. It details the Core Mondrian algorithm for scalable partition-based anonymization, extending the classic Mondrian algorithm with an extensible architecture, parallel processing, and NaN-pattern pre-partitioning. It also presents a data quality framework with three primary metrics (Pearson correlation, RILM, NMIv1) and an empirical validation methodology to ensure anonymized data supports valid statistical conclusions. The library enables successive enforcement of k-anonymity and p-sensitive k-anonymity.