Core Mondrian: Basic Mondrian beyond k-anonymity

Adam Bloomston; Elizabeth Burke; Megan Cacace; Anne Diaz; Wren Dougherty; Matthew Gonzalez; Remington Gregg; Yeliz Güngör; Bryce Hayes; Eeway Hsu; Oron Israeli; Heesoo Kim; Sara Kwasnick; Joanne Lacsina; Demma Rosa Rodriguez; Adam Schiller; Whitney Schumacher; Jessica Simon; Maggie Tang; Skyler Wharton; Marilyn Wilcken

arXiv · 2510.09661

Core Mondrian: Basic Mondrian beyond k-anonymity

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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.

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Adam Bloomston, Elizabeth Burke, Megan Cacace, Anne Diaz, Wren Dougherty, Matthew Gonzalez, Remington Gregg, Yeliz Güngör, Bryce Hayes, Eeway Hsu, Oron Israeli, Heesoo Kim, Sara Kwasnick, Joanne Lacsina, Demma Rosa Rodriguez, Adam Schiller, Whitney Schumacher, Jessica Simon, Maggie Tang, Skyler Wharton, Marilyn Wilcken. 2025-10-07. Core Mondrian: Basic Mondrian beyond k-anonymity. https://arxiv.org/abs/2510.09661

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