{"ID":2859458,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09661","arxiv_id":"2510.09661","title":"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.","short_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...","url_abs":"https://arxiv.org/abs/2510.09661","url_pdf":"https://arxiv.org/pdf/2510.09661v1","authors":"[\"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\"]","published":"2025-10-07T16:47:14Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}