Core Mondrian: Basic Mondrian beyond k-anonymity
Abstract
We present Core Mondrian, a scalable extension of the Original Mondrian partition-based anonymization algorithm. A modular strategy layer supports k-anonymity, allowing new privacy models to be added easily. A hybrid recursive/queue execution engine exploits multi-core parallelism while maintaining deterministic output. Utility-preserving enhancements include NaN-pattern pre-partitioning, metric-driven cut scoring, and dynamic suppression budget management. Experiments on the 48k-record UCI ADULT dataset and synthetically scaled versions up to 1M records achieve lower Discernibility Metric scores than Original Mondrian for numeric quasi-identifier sets while parallel processing delivers up to 4x speedup vs. sequential Core Mondrian. Core Mondrian enables privacy-compliant equity analytics at production scale.
Cite
@article{arxiv.2510.09661,
title = {Core Mondrian: Basic Mondrian beyond k-anonymity},
author = {Adam Bloomston and Elizabeth Burke and Megan Cacace and Anne Diaz and Wren Dougherty and Matthew Gonzalez and Remington Gregg and Yeliz Güngör and Bryce Hayes and Eeway Hsu and Oron Israeli and Heesoo Kim and Sara Kwasnick and Joanne Lacsina and Demma Rosa Rodriguez and Adam Schiller and Whitney Schumacher and Jessica Simon and Maggie Tang and Skyler Wharton and Marilyn Wilcken},
journal= {arXiv preprint arXiv:2510.09661},
year = {2025}
}