通过提示和上下文处理及LLM路由实现隐私保护与令牌节约
Privacy Guard & Token Parsimony by Prompt and Context Handling and LLM Routing
AI总结：
本文提出隐私守护机制，结合上下文管理和LLM路由，通过自动提示优化减少云服务成本和敏感信息泄露风险，实验证明在隐私保护与成本节约之间取得平衡。
摘要：
大规模采用大语言模型（LLM）迫使在运营成本（OpEx）和数据隐私之间做出权衡。当前路由框架减少成本但忽略提示敏感性，暴露用户和机构面临第三方云服务提供商的数据泄露风险。我们正式提出“不可分割范式”：先进的上下文管理本质上与隐私管理密不可分。我们提出一个本地的“隐私守护”机制——一个由本地小型语言模型（SLM）驱动的上下文观察者，通过抽象总结和自动提示优化（APO）将提示分解为聚焦子任务，并将高风险查询重新路由至零信任或NDA覆盖模型。这种双重机制同时消除敏感推断向量（零泄露）并减少云服务令牌负载（OpEx减少）。基于LIFO的上下文压缩机制进一步限制工作内存，限制新兴泄露表面积。我们通过2x2基准测试（懒惰 vs. 专家用户；个人 vs. 机构秘密）在1000个样本数据集上验证了该框架，实现了45%的混合OpEx减少，100%的个人秘密擦除成功，并通过LLM-as-a-Judge评估，APO压缩响应在85%的情况下优于原始基线。我们的结果表明，令牌节约和零泄露是同一上下文压缩操作符的数学双投影。
英文摘要：
The large-scale adoption of Large Language Models (LLMs) forces a trade-off between operational cost (OpEx) and data privacy. Current routing frameworks reduce costs but ignore prompt sensitivity, exposing users and institutions to leakage risks towards third-party cloud providers. We formalise the "Inseparability Paradigm": advanced context management intrinsically coincides with privacy management. We propose a local "Privacy Guard" -- a holistic contextual observer powered by an on-premise Small Language Model (SLM) -- that performs abstractive summarisation and Automatic Prompt Optimisation (APO) to decompose prompts into focused sub-tasks, re-routing high-risk queries to Zero-Trust or NDA-covered models. This dual mechanism simultaneously eliminates sensitive inference vectors (Zero Leakage) and reduces cloud token payloads (OpEx Reduction). A LIFO-based context compacting mechanism further bounds working memory, limiting the emergent leakage surface. We validate the framework through a 2x2 benchmark (Lazy vs. Expert users; Personal vs. Institutional secrets) on a 1,000-sample dataset, achieving a 45% blended OpEx reduction, 100% redaction success on personal secrets, and -- via LLM-as-a-Judge evaluation -- an 85% preference rate for APO-compressed responses over raw baselines. Our results demonstrate that Token Parsimony and Zero Leakage are mathematically dual projections of the same contextual compression operator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
