Privacy-Preserving LLMs Routing

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

PPRoute is a novel privacy-preserving framework designed for Large Language Model (LLM) routing, addressing the privacy risks introduced by intermediate routing layers. LLM routing dynamically selects services from various model providers to balance performance and cost, but typically involves third-party management, creating data leakage vulnerabilities. PPRoute leverages Secure Multi-Party Computation (MPC) to protect user queries during inference, overcoming the prohibitive computational overhead usually associated with MPC. It achieves this through three main strategies: using MPC-friendly operations to accelerate encoder inference, employing a multi-step model training algorithm to maintain routing quality in encrypted domains, and introducing an unsorted Top-k algorithm with O(1) communication complexity for secure nearest neighbor search. PPRoute demonstrates comparable performance to plaintext counterparts while achieving approximately a 20x speedup over naive MPC implementations across various datasets like EmbedLLM, MixInstruct, and RouterBench.

Key takeaway

For AI Architects and Research Scientists designing secure LLM deployment strategies, PPRoute offers a validated approach to mitigate privacy risks in routing without sacrificing performance. Your teams can adopt its MPC-friendly encoder approximations, multi-stage training, and O(1) communication complexity unsorted Top-k algorithm to build robust, privacy-preserving LLM routing systems. This framework allows you to balance cost-efficiency and model capability while ensuring end-to-end data privacy for sensitive applications.

Key insights

PPRoute enables privacy-preserving LLM routing via MPC-friendly operations, multi-stage training, and an efficient unsorted Top-k algorithm.

Principles

• MPC can be optimized for LLM inference.
• Approximations can maintain model accuracy.
• Communication complexity is key for secure sorting.

Method

PPRoute optimizes transformer encoder inference by replacing Softmax and GeLU with "2ReLU" and ReLU, respectively. It uses a multi-stage distillation algorithm for training and an unsorted Top-k algorithm for nearest neighbor search, achieving O(1) communication complexity.

In practice

• Replace expensive activation functions with MPC-friendly ones.
• Use multi-stage distillation for robust model training.
• Implement unsorted Top-k for efficient secure retrieval.

Topics

• Privacy-Preserving LLM Routing
• Secure Multi-Party Computation
• MPC-friendly Operations
• Unsorted Top-k Algorithm
• Embedding-based LLM Routing

Best for: AI Architect, Research Scientist, CTO, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.