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English(EN) P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems

P2Skill方法增强了本地LLM推理的隐私性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了P2Skill，一种用于云本地LLM推理系统的隐私保护技能蒸馏的新颖方法。该方法通过分解任务、路由PII感知信息和重建输出来处理敏感数据，而无需进行隐私特定的微调。P2Skill根据云LLM执行失败的情况迭代地改进技能，使本地SLM能够泛化超出已知的PII模式。评估表明，P2Skill在隐私保护推理质量方面显著优于以前的方法。 AI

影响 通过在无需微调的情况下实现超出已记忆PII模式的泛化，增强了本地LLM推理的隐私性。

排序理由 详细介绍LLM推理新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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P2Skill方法增强了本地LLM推理的隐私性

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详细介绍LLM推理新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Myunghoon Ryu, Geunpyo Park, Sungjoon Lee, XinYu Piao, Jong-Kook Kim ·

    P2Skill：云端本地LLM推理系统的隐私保护技能蒸馏

    arXiv:2608.14094v1 Announce Type: cross Abstract: Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on personal devices. Cloud-bound requests must exclude personally identifiable informa…

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