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New GBDT Training Method Hides Record IDs for Enhanced Privacy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for training Gradient Boosting Decision Trees (GBDTs) on vertically partitioned data while preserving the anonymity of record identifiers. This approach addresses the security vulnerabilities of existing methods that rely on Private Set Intersection (PSI), which can inadvertently expose shared IDs. The proposed protocol uses a dual circuit-PSI design and oblivious programmable pseudorandom functions to enable secure, ID-hiding aggregation, offering a more efficient and private solution for sensitive data analytics in fields like finance and healthcare. AI

RANK_REASON This is a research paper detailing a new technical approach to secure machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GBDT Training Method Hides Record IDs for Enhanced Privacy

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This is a research paper detailing a new technical approach to secure machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Huang Chenyu, Zhang Fan, Du Minxin, Chow Sherman SM, Chen Huangxun, Rao Huaming, Huang Danqing, Qian Bo, Chen Peng ·

    Practical Anonymous Two-Party Gradient Boosting Decision Tree

    arXiv:2605.26903v1 Announce Type: cross Abstract: Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High speed and interpretability make GBDTs popular in fin…

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