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This paper introduces the Sensitive Entity Alias Generator (SEAG), a framework designed to enhance privacy in Retrieval-Augmented Generation (RAG) by concealing sensitive information from external large language models (LLMs). SEAG employs a lightweight model to identify sensitive entities, generate aliases, and create an entity replacement table, which effectively replaces sensitive terms in user queries and retrieved documents before they reach external generators. Experimental results indicate that SEAG models maintain over 80% accuracy in providing correct responses while successfully hiding sensitive information, demonstrating a significant advancement in privacy-preserving AI applications.
SEAG enables users to leverage powerful external LLMs without compromising sensitive data, achieving over 80% accuracy in user responses while concealing confidential information.
Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing privacy research on RAG has focused on preventing unauthorized users from accessing sensitive data. However, another important problem that is often overlooked in RAG privacy research is that external generators have access to the query and the retrieved documents, which may contain confidential information that could potentially be misused or accessed for unintended purposes. In this paper, we introduce the Sensitive Entity Alias Generator (SEAG), a privacy-preserving framework that empowers users to utilize powerful third-party generators without disclosing sensitive information. SEAG introduces a lightweight model that locates sensitive entities, generates corresponding aliases, and constructs an entity replacement table. The table is used to replace sensitive words in the user's query and in the retrieved documents before they are forwarded to an external generator. For this purpose, two datasets were constructed: one for fine-tuning SEAG models to generate entity replacement tables, and another for evaluating the entire SEAG framework. The experimental results demonstrate the success of the SEAG framework. As for the User metric, which measures the ability of the model to provide a correct response to the user while hiding sensitive information from the external generator, all SEAG models achieved over 80% accuracy. Additional analysis further evaluated the ability of SEAG models Qwen-3, LLaMA-3.2, and Phi-4 to hide all sensitive entities within given documents. The results show good performance with total accuracies of 77.83%, 76.73%, and 74.91%, respectively.
