基于上下文完整性，智能删减大模型请求中的敏感信息。
Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation
- 根据任务必要性判断敏感内容是否该保留
- 在3167个样本上训练，隐私保护下提升10.1%任务效率
- 适合需要保护隐私的云端大模型应用开发者
随着大模型融入日常使用，用户向云端模型提交的查询常混合任务必需内容与非必需敏感信息。现有基于类型的个人身份信息（PII）删除方法缺乏上下文感知，可能导致过度披露或误删关键回答内容。本文提出基于上下文完整性的隐私保护查询重写框架：仅当片段对任务必要时才转发。构建首个面向隐私委托的任务型上下文完整性基准 DelegateCI-Bench，包含3,167个样本，涵盖11项任务、20种任务类型，结合高质量合成数据、真实用户对话（WildChat）及高密度敏感信息的医疗挑战集。基于此基准，设计一种受上下文完整性引导的强化学习框架，将必要与非必要敏感片段转化为可验证优化信号，训练查询重写器在保留任务关键信息的同时抑制不必要的敏感披露。实验表明，所提学习重写器在隐私-效用权衡上表现最佳，平均效用相比本地基线最高提升10.1%。
原文摘要 · Abstract (English)
As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-essential sensitive disclosures, yet type based PII redaction is context agnostic and may raise two issues: over disclosing untyped sensitive context and over removing answer bearing spans. We recast privacy preserving query rewriting under Contextual Integrity: a span should be forwarded only if it is necessary for the task. We introduce DelegateCI-Bench, the first task based Contextual Integrity benchmark for privacy-conscious delegation, comprising 3,167 samples that combine high quality synthetic data spanning 11 tasks and 20 task types, WildChat based real user queries, and a medical challenge set with dense sensitive information. Building on this benchmark, we propose a CI-guided reinforcement learning framework that converts essential and non-essential sensitive spans into verifiable optimization signals, and train a query rewriter to preserve task critical information while suppressing unnecessary sensitive disclosure. Experiments show that our learned rewriter achieves the best privacy-utility tradeoff, achieving up to +10.1 average utility over on-device baselines.
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