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English(EN) Practical Anonymous Two-Party Gradient Boosting Decision Tree

新的GBDT训练方法隐藏记录ID以增强隐私

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种在垂直划分数据上训练梯度提升决策树（GBDTs）的新方法，同时保护记录标识符的匿名性。该方法解决了现有依赖私有集合交集（PSI）的方法存在的安全漏洞，这些方法可能会无意中暴露共享ID。提出的协议使用双电路PSI设计和不可知可编程伪随机函数，实现安全的、隐藏ID的聚合，为金融和医疗保健等领域的敏感数据分析提供了更高效、更私密的解决方案。 AI

排序理由 这是一篇详细介绍安全机器学习新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新的GBDT训练方法隐藏记录ID以增强隐私

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报道来源 [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 ·

    实用匿名两方梯度提升决策树

    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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