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New CoRTeC method generates differentially private synthetic tabular data

Researchers have developed CoRTeC, a novel method for releasing synthetic tabular data that adheres to differential privacy standards. This approach limits the privacy budget by releasing a small, noisy file of counts, which can then be used by any generator, including language models, to create synthetic data without direct access to real records. The synthetic data generated by CoRTeC has demonstrated strong performance in training classifiers, achieving AUC scores close to those trained on real data, while effectively protecting individual privacy. AI

IMPACT Enables secure sharing and utilization of sensitive tabular data for AI model training.

RANK_REASON The cluster describes a new method for generating synthetic data with differential privacy, detailed in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New CoRTeC method generates differentially private synthetic tabular data

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The cluster describes a new method for generating synthetic data with differential privacy, detailed in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Calister Nnona ·

    CoRTeC: A Differentially Private Release for Synthetic Tabular Data, Decodable by Any Generator

    arXiv:2611.06342v1 Announce Type: cross Abstract: The records that could train the most useful models, such as a hospital's patient files, are often the ones an institution cannot share. We propose CoRTeC, a method that releases synthetic versions of such records under differenti…