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English(EN) De-biased Distribution Estimation for Locally Differential Private Survival Data

新估计器改进了生存数据分析的隐私保护

研究人员开发了一种新的非参数估计器,旨在对经过局部差分隐私 (LDP) 处理的生存数据的分布估计进行去偏。该方法解决了 LDP 内在扰动导致的效用损失和统计推断问题,尤其适用于右删失生存数据。所提出的估计器同时校正了事件时间和事件指示符中引入的噪声,理论上建立了均匀一致性和渐近正态性。通过模拟和 METABRIC 乳腺癌队列的分析证明了其性能,显示了在各种隐私预算、删失水平和样本量下的有效性。 AI

影响 增强了敏感临床数据分析的隐私保护技术,有可能通过受保护的数据集实现更 robust 的研究。

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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新估计器改进了生存数据分析的隐私保护

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该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yi Xiong, Li-Pang Chen, Liyue Fan ·

    用于局部差分隐私生存数据的去偏分布估计

    arXiv:2610.14320v1 Announce Type: cross Abstract: Sharing time-to-event data can leak sensitive information about individual participants in clinical studies. To protect the privacy of data, differential privacy (DP) framework offers rigorous protection for data sharing. The loca…