RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service (2412.12775v1)

Abstract: Retrieval-augmented generation (RAG) improves the service quality of LLMs by retrieving relevant documents from credible literature and integrating them into the context of the user query. Recently, the rise of the cloud RAG service has made it possible for users to query relevant documents conveniently. However, directly sending queries to the cloud brings potential privacy leakage. In this paper, we are the first to formally define the privacy-preserving cloud RAG service to protect the user query and propose RemoteRAG as a solution regarding privacy, efficiency, and accuracy. For privacy, we introduce $(n,\epsilon)$-DistanceDP to characterize privacy leakage of the user query and the leakage inferred from relevant documents. For efficiency, we limit the search range from the total documents to a small number of selected documents related to a perturbed embedding generated from $(n,\epsilon)$-DistanceDP, so that computation and communication costs required for privacy protection significantly decrease. For accuracy, we ensure that the small range includes target documents related to the user query with detailed theoretical analysis. Experimental results also demonstrate that RemoteRAG can resist existing embedding inversion attack methods while achieving no loss in retrieval under various settings. Moreover, RemoteRAG is efficient, incurring only $0.67$ seconds and $46.66$KB of data transmission ($2.72$ hours and $1.43$ GB with the non-optimized privacy-preserving scheme) when retrieving from a total of $10^6$ documents.

• The paper formally defines privacy risks in cloud RAG and introduces an (n,ε)-DistanceDP framework using Laplace perturbation to protect sensitive query embeddings.
• The paper demonstrates that RemoteRAG achieves comparable retrieval accuracy to non-private models while significantly reducing computational overhead.
• The results show that RemoteRAG efficiently secures user queries via homomorphic encryption and differential privacy, achieving a latency of 0.67 seconds with minimal data transmission.

An Essay on "RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service"

The paper, "RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service," addresses one of the pressing issues in leveraging Retrieval-Augmented Generation (RAG) with LLMs: maintaining user privacy. RAG has become an essential technique for improving the factual accuracy of LLM outputs by integrating extracted knowledge from external databases. With the advent of RAG-as-a-Service (RaaS), concerns about the privacy of user queries sent to cloud servers have gained prominence. This paper introduces a formal framework for a privacy-preserving cloud RAG service and proposes "RemoteRAG" as a comprehensive solution to achieve efficiency, accuracy, and enhanced privacy protection.

Core Contributions

The key contributions of the paper can be distilled into three major areas: formal problem definition, theoretical privacy framework, and empirical demonstration of RemoteRAG's efficacy.

1. Formal Definition of the Privacy Issue: The authors are pioneers in formally defining the privacy concerns associated with cloud RAG services. They lay out the potential risks wherein user queries could reveal sensitive information like health or financial data. The threat model anticipates a semi-honest but curious cloud server that seeks to infer these sensitive queries.
2. $(n,\epsilon)$-DistanceDP Framework: Inspired by differential privacy, the paper defines $(n,\epsilon)$-DistanceDP, a mechanism for characterizing privacy leakage via perturbed query embeddings. The perturbation is generated by exploiting the Laplace mechanism in an $n$-dimensional space. This deepens the understanding of how differential privacy concepts can be repurposed for embedding-based retrieval systems.
3. Efficient and Accurate Retrieval Mechanism: RemoteRAG aims to reduce computational overhead by limiting the search range to a smaller subset of potentially relevant documents. The paper rigorously proves that this subset is sufficient to include the top $k$ documents pertinent to the user's query. It also employs homomorphic encryption for secure computation, thereby protecting the user's input query embedding from unnecessary exposition.

Experimental Highlights

The experimental evaluation substantiates RemoteRAG's efficacy across various metrics: privacy preservation, retrieval accuracy, and service efficiency. Remarkably, the proposed system achieves no loss in retrieval accuracy compared to non-private baselines, all while enforcing differentially private guarantees. Communication and computational overheads are kept minimal, demonstrating the service's practicality with only $0.67$ seconds of latency and modest data transmission demands.

For researchers exploring the practical implementation of private RAG systems, the empirical part of this paper serves as an invaluable reference. The use of publicly available datasets and existing LLMs like MiniLM, MPNet, and OpenAI's text-embedding models augments reproducibility and broader applicability of the results.

Implications and Future Directions

"RemoteRAG" extends current RAG frameworks by introducing the dimension of privacy-preserving computation, which is crucial for its delegation to third-party cloud services. Theoretical implications include paving pathways for integrating differential privacy principles with embedding-based retrieval, which can be extended to various other AI services requiring external data augmentation.

Practically, the remote model offers insights into constructing data privacy protocols, which can aid organizations dealing with sensitive informations, such as healthcare providers and financial institutions, in deploying AI solutions confidently.

This foundational research on cloud-based privacy can inspire future developments in both RAG optimization and privacy-preserving computations. There remains room for exploration in enhancing the balance between retrieval accuracy and privacy, potentially via adaptive perturbation techniques. Further research could also investigate optimizing homomorphic encryption implementations to decrease computational overhead further, making this approach attractive for real-time AI applications.

In summary, "RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service" provides a robust methodological framework and an effective practical solution that addresses the privacy concerns associated with leveraging RAG in LLMs. This paper is an insightful addition to the ongoing research on privacy-preserving AI, and it sets the stage for future investigations in this domain.