Privacy-Preserving LLMs Routing
Summary: arXiv:2604.15728v1 Announce Type: cross
Abstract: Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting services from various model providers. However, LLM routing adds an intermediate layer between users and LLMs, creating new privacy risks to user data. These privacy risks have not been systematically studied. Although cryptographic techniques such as Secure Multi-Party Computation (MPC) enable privacy-preserving computation, their protocol design and implementation remain under-explored, and naive implementations typically incur prohibitive computational overhead. To address this, we propose a privacy-preserving LLM routing framework (PPRoute).
Introduction to LLM Routing
LLM routing is an innovative approach that allows for the selection of the most suitable model based on specific user requirements while optimizing for both performance and cost. By leveraging services from various model providers, users can benefit from improved efficiency and effectiveness in their applications. However, with this added layer of complexity, new privacy challenges arise, particularly concerning the handling of sensitive user data.
Privacy Risks in LLM Routing
The introduction of an intermediate layer in LLM routing can expose user data to potential vulnerabilities. Key privacy risks include:
- Data Leakage: User inputs may be inadvertently exposed during routing processes.
- Unauthorized Access: Malicious actors could exploit routing mechanisms to gain access to private information.
- Lack of Transparency: Users may not be fully aware of how their data is being processed and routed.
Despite the existence of cryptographic solutions such as Secure Multi-Party Computation (MPC), the practical implementation of these techniques has not been sufficiently explored, often leading to inefficient and costly computations.
Introducing PPRoute
To mitigate the privacy risks associated with LLM routing, we introduce PPRoute, a privacy-preserving framework designed to enhance user data protection while maintaining performance efficiency. PPRoute employs several innovative strategies:
- MPC-Friendly Operations: The framework utilizes operations that are compatible with MPC to enhance the speed of encoder inference, thereby minimizing delays.
- Multiple-Step Model Training: PPRoute implements a training algorithm that ensures routing quality is preserved, even when operating within the constraints of encrypted data.
- Unsorted Top-k Algorithm: This novel approach allows for secure sorting in model searches with $O(1)$ communication complexity, significantly reducing the latency associated with data transmission.
Performance Evaluation
In extensive testing across various datasets, PPRoute has demonstrated its ability to match the performance of traditional, plaintext counterparts. Remarkably, it achieves approximately a 20× speedup over naive MPC implementations, making it a compelling solution for organizations seeking to enhance their LLM routing capabilities without compromising user privacy.
Conclusion
As the reliance on large language models continues to grow, addressing the associated privacy risks is paramount. The PPRoute framework represents a significant step forward in enabling secure, efficient, and effective LLM routing, ensuring that user data remains protected while reaping the benefits of advanced model technologies.