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English(EN) Mitigating Private Data Leakage in LLMs with Whiteout

新的Whiteout工具可防止LLM泄露私有数据

一篇新的研究论文介绍了一种名为Whiteout的工具,该工具旨在防止大型语言模型(LLM)泄露私有的敏感信息(PSI)。与现有方法通常会降低模型性能或容易受到攻击不同,Whiteout使用精确的混淆样本来覆盖PSI。在包括OpenAI模型在内的各种LLM上进行测试,Whiteout能够有效阻止目标PSI的泄露,同时对效用和安全性影响最小,并且在对抗一系列对抗措施方面优于现有替代方案。 AI

影响 增强了LLM的隐私保护,通过降低敏感数据暴露的风险,有可能增加用户信任和采用率。

排序理由 该集群是关于一篇详细介绍缓解LLM数据泄露新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 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Whiteout工具可防止LLM泄露私有数据

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该集群是关于一篇详细介绍缓解LLM数据泄露新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anna Yoo Jeong Ha, Ronik Bhaskar, Haitao Zheng, Ben Y. Zhao ·

    使用 Whiteout 缓解 LLM 中的私有数据泄露

    arXiv:2610.02418v1 Announce Type: cross Abstract: Modern large language models (LLMs) are trained on massive, largely unfiltered datasets, including content scraped from nearly every accessible website and user inputs. As a result, LLMs often memorize and reproduce personally sen…