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English(EN) Veil-Then-Reveal: Protecting PII Privacy via Reversible Pseudo-Anonymization in Zero-Trust User--LLM Interaction

新框架通过可逆伪匿名化保护LLM交互中的PII

提出了一种名为Veil-then-Reveal (VtR)的新框架,用于在与大型语言模型 (LLM) 的交互中保护个人身份信息 (PII)。这种用户端方法旨在通过在敏感数据离开用户设备之前对其进行伪匿名化,来防止将敏感数据发送到远程LLM提供商的风险。VtR用逼真的替代物替换PII,同时保留语言和结构属性,并允许在LLM响应中可逆地恢复原始PII。 AI

影响 该框架可以增强用户与LLM交互时的隐私性,可能增加LLM技术在敏感数据处理方面的信任度和采用率。

排序理由 该集群包含一篇详细介绍LLM交互新技术框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

新框架通过可逆伪匿名化保护LLM交互中的PII

本文如何被排名

Signal score
11 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该集群包含一篇详细介绍LLM交互新技术框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, safety
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mohamed Shaaban, Mohamed Elmahallawy, Thomas E. Carroll ·

    Veil-Then-Reveal:通过零信任用户-LLM交互中的可逆伪匿名化保护PII隐私

    arXiv:2610.22800v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly process sensitive documents and personal information, yet sending prompts containing personally identifiable information (PII) to remote providers exposes data subjects and organizations t…