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 $p^2$RAG, a privacy-preserving RAG service that supports arbitrary top-$k$ retrieval. Unlike existing systems, $p^2$RAG avoids sorting candidate documents. Instead, it uses an interactive bisection method to determine the set of top-$k$ documents. For security, $p^2$RAG 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 $p^2$RAG is 3--300$\times$ faster than the state-of-the-art PRAG for $k = 16$--$1024$.
Workflow Figure #
During the offline stage, the data owner sets up the secret-shared database. During the distance calculation, the servers compute the secret-shared distances between each document and the user's prompt. During the distance bisection, the user determines a distance threshold $d_k$ for the top-$k$ documents. The bisection iteration ends when $d_k$ is found, or the number of iterations exceeds an upper bound. During the text retrieval, the user retrieves textual documents using the indices of the top-$k$ documents.
Full Paper #
The preprint PDF and LaTeX sources is available here and on arXiv (coming soon).