FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation (2410.13272v1)

Abstract: This paper introduces \textit{Federated Retrieval-Augmented Generation (FRAG)}, a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems, which are increasingly powered by large-LLMs. FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted query vectors and encrypted data stored in distributed vector databases, all while ensuring that no party can gain any knowledge about the queries or data of others. Achieving this paradigm presents two key challenges: (i) ensuring strong security guarantees, such as Indistinguishability under Chosen-Plaintext Attack (IND-CPA), under practical assumptions (e.g., we avoid overly optimistic assumptions like non-collusion among parties); and (ii) maintaining performance overheads comparable to traditional, non-federated RAG systems. To address these challenges, FRAG employs a single-key homomorphic encryption protocol that simplifies key management across mutually-distrusted parties. Additionally, FRAG introduces a \textit{multiplicative caching} technique to efficiently encrypt floating-point numbers, significantly improving computational performance in large-scale federated environments. We provide a rigorous security proof using standard cryptographic reductions and demonstrate the practical scalability and efficiency of FRAG through extensive experiments on both benchmark and real-world datasets.

• The paper introduces FRAG using the SK-MHE protocol to simplify key management and enable secure ANN searches on encrypted data.
• The MC protocol reduces computational overhead by caching intermediary encrypted values, accelerating intensive floating-point operations.
• Experimental results on datasets like MNIST and CIFAR-10 demonstrate FRAG's practical performance in federated, privacy-preserving retrieval tasks.

Federated Retrieval-Augmented Generation (FRAG): A Novel Approach to Secure ANN Searches

The paper presents an innovative approach to Federated Retrieval-Augmented Generation (FRAG), focusing on balancing secure data processing with efficient performance in distributed environments. This work addresses the dual challenges of maintaining high-level security standards and achieving performance efficiency in federated vector database environments that support retrieval-augmented generation (RAG) systems. The proposed methods leverage robust cryptographic techniques to enable mutually-distrusted parties to perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted data, ensuring privacy without sacrificing computational speed.

Key Innovations

The FRAG framework introduces two primary innovations: the Single-Key Multiparty Homomorphic Encryption (SK-MHE) protocol and the Multiplicative Caching (MC) protocol.

1. Single-Key Homomorphic Encryption (SK-MHE): This protocol simplifies key management in federated environments by allowing all parties to use a shared encryption key. It upholds strong security guarantees such as Indistinguishability under Chosen-Plaintext Attack (IND-CPA). The SK-MHE protocol efficiently supports homomorphic operations necessary for performing ANN searches on encrypted vectors, leveraging cryptographic reductions to prove security while minimizing performance overhead.
2. Multiplicative Caching (MC): The MC protocol is designed to lower the computational costs associated with homomorphic encryption by caching encrypted intermediary values. This approach enhances the practicality of FRAG in large-scale implementations by reducing redundancies in homomorphic multiplications, crucial for tasks involving extensive floating-point computations, therefore optimizing the workflows in real-time ANN searches.

Experimental Evaluation

The evaluation was conducted using the FRAG system on various datasets, such as MNIST, FMNIST, CIFAR-10, and SVHN. The performance of cryptographic primitives and computational overhead introduced by SK-MHE were thoroughly examined. The results demonstrate that while SK-MHE adds some overhead compared to traditional non-encrypted approaches, it remains efficient enough for practical application, achieving competitive performance.

Further, the scalability and efficiency of the MC protocol were validated across different real-world datasets (Covid-19, Bitcoin, and Human Gene #38), showing substantial speedups in computation. This is indicative of the protocol’s ability to handle large, dynamic datasets effectively, often necessary in federated environments with high query volumes.

Implications and Future Directions

The practical implications of FRAG extend to multiple domains requiring secure, collaborative data retrieval, such as healthcare analytics, financial institutions, and legal services. The capability to execute encrypted ANN searches without revealing sensitive information paves the way for broader adoption of retrieval-augmented systems in privacy-sensitive scenarios.

Theoretically, this work contributes to ongoing research in secure multiparty computation and homomorphic encryption. By tackling the overhead problems inherent in traditional cryptographic methods, it fosters advancements towards real-world applicability of privacy-preserving AI systems.

Future research could explore further enhancements in caching strategies or the integration of parallel processing techniques to reduce latency further. Additionally, extending compatibility with other federated learning frameworks could broaden use cases and maintain the momentum in federated AI development.

In summary, FRAG offers a compelling solution for securely maximizing the utility of distributed data siloes, effectively blending advanced cryptographic techniques with the practical requirements of modern AI systems. This work exemplifies the potential to achieve secure, federated learning without compromising on performance, which could set a precedent for future innovations in the domain.