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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09661 |
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| _version_ | 1866908587277680640 |
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| author | Bloomston, Adam Burke, Elizabeth Cacace, Megan Diaz, Anne Dougherty, Wren Gonzalez, Matthew Gregg, Remington Güngör, Yeliz Hayes, Bryce Hsu, Eeway Israeli, Oron Kim, Heesoo Kwasnick, Sara Lacsina, Joanne Rodriguez, Demma Rosa Schiller, Adam Schumacher, Whitney Simon, Jessica Tang, Maggie Wharton, Skyler Wilcken, Marilyn |
| author_facet | Bloomston, Adam Burke, Elizabeth Cacace, Megan Diaz, Anne Dougherty, Wren Gonzalez, Matthew Gregg, Remington Güngör, Yeliz Hayes, Bryce Hsu, Eeway Israeli, Oron Kim, Heesoo Kwasnick, Sara Lacsina, Joanne Rodriguez, Demma Rosa Schiller, Adam Schumacher, Whitney Simon, Jessica Tang, Maggie Wharton, Skyler Wilcken, Marilyn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Core Mondrian: Basic Mondrian beyond k-anonymity Bloomston, Adam Burke, Elizabeth Cacace, Megan Diaz, Anne Dougherty, Wren Gonzalez, Matthew Gregg, Remington Güngör, Yeliz Hayes, Bryce Hsu, Eeway Israeli, Oron Kim, Heesoo Kwasnick, Sara Lacsina, Joanne Rodriguez, Demma Rosa Schiller, Adam Schumacher, Whitney Simon, Jessica Tang, Maggie Wharton, Skyler Wilcken, Marilyn Cryptography and Security 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. |
| title | Core Mondrian: Basic Mondrian beyond k-anonymity |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.09661 |