研究匿名化在RAG流程中的位置如何影响隐私与效果的平衡。
A Case Study on the Impact of Anonymization Along the RAG Pipeline
- 在数据集和生成答案两个环节分别做匿名化对比
- 不同位置的匿名化导致隐私保护程度与信息可用性差异明显
- 提醒RAG管理员需重视匿名化策略的部署位置
尽管检索增强生成（RAG）具有巨大潜力，但许多实际应用场景可能引发隐私问题，即RAG生成的洞察虽有用，却可能暴露个人敏感信息给大模型或最终用户。为缓解这一风险，对底层数据中的个人身份信息（PII）及其他敏感标记进行匿名化处理，是RAG管理者可行且合理的做法。尽管该领域已有大量研究，但尚无工作探讨匿名化在RAG流程中的具体位置——即匿名化应放在何处？本案例研究系统性地、实证地评估了在RAG流程两个关键节点实施匿名化的影响：数据集阶段与生成回答阶段。结果表明，匿名化的位置不同会带来显著不同的隐私-效用权衡，凸显了隐私风险缓解策略部署位置的重要性。
原文摘要 · Abstract (English)
Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported utility of RAG-enabled insights comes at the risk of exposing private information to either the LLM or the end user requesting the response. As a potential mitigation, using anonymization techniques to remove personally identifiable information (PII) and other sensitive markers in the underlying data represents a practical and sensible course of action for RAG administrators. Despite a wealth of literature on the topic, no works consider the placement of anonymization along the RAG pipeline, i.e., asking the question, where should anonymization happen? In this case study, we systematically and empirically measure the impact of anonymization at two important points along the RAG pipeline: the dataset and generated answer. We show that differences in privacy-utility trade-offs can be observed depending on where anonymization took place, demonstrating the significance of privacy risk mitigation placement in RAG.
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