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English(EN) QuanText: Protecting Dataset-Level Secrets in Textual Data Sharing

新的QuanText方法保护文本数据集中的敏感数据

研究人员开发了QuanText,一种用于保护文本数据集中敏感信息的新颖方法。这种无需训练的方法旨在与任何大型语言模型兼容,并专注于保护全局数据集属性,例如与特定人群或主题相关的记录比例。QuanText通过扰乱秘密分布和相关属性来工作,确保在模糊敏感的聚合信息的同时,保持数据在下游应用程序中的效用。 AI

影响 增强了文本数据集的隐私性,能够在不损害效用的情况下实现更安全的数据共享和研究。

排序理由 该项目描述了一篇详细介绍新数据隐私方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的QuanText方法保护文本数据集中的敏感数据

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该项目描述了一篇详细介绍新数据隐私方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shuaiqi Wang, Zinan Lin, Giulia Fanti ·

    QuanText:保护文本数据共享中的数据集级隐私

    arXiv:2609.17995v1 Announce Type: new Abstract: Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the underlying data source, such as the proportion of records associated with a partic…