p^2RAG: Privacy-Preserving RAG Service Supporting Arbitrary Top-k Retrieval

Abstract

Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy concerns for both data owners and users. Privacy-preserving RAG systems address these concerns by performing secure top-k retrieval, which typically is secure sorting to identify relevant documents. However, existing systems face challenges supporting arbitrary k due to their inability to change k, new security issues, or efficiency degradation with large k. This is a significant limitation because modern long-context models generally achieve higher accuracy with larger retrieval sets. We propose p^2RAG, a privacy-preserving RAG service that supports arbitrary top-k retrieval. Unlike existing systems, p^2RAG avoids sorting candidate documents. Instead, it uses an interactive bisection method to determine the set of top-k documents. For security, p^2RAG uses secret sharing on two semi-honest non-colluding servers to protect the data owner's database and the user's prompt. It enforces restrictions and verification to defend against malicious users and tightly bound the information leakage of the database. The experiments show that p^2RAG is 3--300 faster than the state-of-the-art PRAG for k = 16--1024.

Reproductions