**Original paper:** [https://arxiv.org/abs/2608.12675v1](https://arxiv.org/abs/2608.12675v1)

# Summary: 2026-08-13_00-23-56Z_Privacy_PreservingRAGbyConcealingSensitiveInformat.md
Saved: 2026-08-13 22:32
Source: 2026-08-13_00-23-56Z_Privacy_PreservingRAGbyConcealingSensitiveInformat.md
Model: None

---

## Summary  
The paper addresses a gap in RAG privacy research by showing that external language models can inadvertently expose sensitive entities from both user queries and retrieved documents. To mitigate this risk, the authors propose SEAG (Sensitive Entity Alias Generator), a lightweight system that anonymizes confidential information before it reaches any third‑party generator. The framework replaces identified sensitive words with generated aliases using an entity replacement table, thereby preserving answer quality while hiding data from external LLMs. Experimental evaluation demonstrates robust performance across multiple state‑of‑the‑art models.

## Key Contributions  
- [Finding 1] SEAG introduces a novel privacy‑preserving mechanism that substitutes sensitive entities in both queries and documents with semantically equivalent aliases, preventing external generators from seeing the original data.  
- [Finding 2] The framework achieves over 80% accuracy on the User metric, indicating that answer quality is maintained while information remains hidden.  
- [Finding 3] SEAG’s entity replacement tables achieve high precision for different models: Qwen‑3 (77.83%), LLaMA‑3.2 (76.73%), and Phi‑4 (74.91%) in hiding all sensitive entities within given documents.

## Methodology  
SEAG consists of two main components: a lightweight model that scans input text to locate sensitive entities, and an alias generation module that creates replacement tokens. The authors first fine‑tune the alias generator on a curated dataset of entity‑replacement pairs derived from real RAG scenarios. During inference, the system builds an entity replacement table specific to each query‑document pair and injects it into both the user prompt and the retrieved documents before forwarding them to any external LLM. The process is fully offline and requires only the original text; no additional storage of sensitive content is needed.

## Results  
The User metric, which measures correct answer generation while ensuring hiddenness, exceeds 80% across all SEAG models. Separate analyses on three benchmark LLMs report total accuracies for hiding every sensitive entity: Qwen‑3 (77.83%), LLaMA‑3.2 (76.73%), and Phi‑4 (74.91%). These results confirm that SEAG effectively preserves both privacy and utility, even when the underlying generator is powerful.

## Significance  
By decoupling the generation of sensitive information from its transmission to external models, SEAG offers a practical solution for organizations that rely on third‑party LLMs while complying with data protection regulations. The lightweight nature of the framework makes it deployable in real‑time RAG pipelines without significant latency or resource overhead.

## Related Concepts  
Retrieval-Augmented Generation (RAG), Sensitive Entity Alias Generator (SEAG), entity replacement tables, privacy‑preserving frameworks, external LLMs, entity detection, semantic alias generation.