新数据集和方法使AI智能体符合人类隐私规范

报道来源 [3]

1. arXiv cs.AI TIER_1 English(EN) · Yuanhe Zhao, Tianyu Zhang, Huafei Xing, Derek F. Wong, Jianbin Li, Tao Fang · 2026-06-24 04:00

   Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

   arXiv:2606.24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent fram…

2. arXiv cs.AI TIER_1 English(EN) · Tao Fang · 2026-06-23 14:21

   Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

   Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through sem…

3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Spandana Gella · 2026-06-19 19:50

   PrivacyAlign：LLM代理的上下文隐私对齐

   AI agents acting on behalf of users are constantly making decisions, and for users to trust their agents, those decisions must align with what they actually want. Privacy is an important alignment problem for agents: every message, post, or tool call an agent makes is a contextua…