PRISM: Privacy-Aware Routing for Adaptive Cloud–Edge LLM Inference with Semantic Sketch Collaboration

📄 [Accepted at 2026 AAAI Conference on Artificial Intelligence] — To appear

This project introduces PRISM, a context-aware cloud–edge inference framework that balances privacy, utility, and efficiency for Large Language Model (LLM) services. It addresses the key limitations of uniform privacy mechanisms by adapting protection based on semantic sensitivity of user inputs.

Objectives

The primary goal is to enable privacy-preserving LLM inference in real-world deployments, where sensitive user prompts are routed intelligently between edge devices and the cloud. PRISM is designed to:

• Avoid unnecessary noise for benign inputs
• Preserve semantic coherence in sensitive prompts
• Reduce latency and energy consumption without compromising utility

Key Contributions

Semantic-Sensitive Execution Routing

• A soft gating controller on the edge scores entity-level risk using contextual features (e.g., named entities, first-person references)
• Routes prompts to one of three execution paths:
  • Edge-only for high-risk prompts
  • Cloud-only for low-risk prompts
  • Cloud–Edge Collaboration for mid-sensitivity prompts

Adaptive Two-Layer Local Differential Privacy (LDP)

• Each sensitive entity is obfuscated through:
  • Category-level perturbation (e.g., masking “Diagnosis”)
  • Value-level perturbation (e.g., replacing “HIV” with “Flu”)
• Privacy budget allocation is guided by a sensitivity weight model ensuring fine-grained protection without semantic collapse

Semantic Sketch Collaboration Protocol

• Noisy prompts are processed in the cloud to generate semantic sketches (e.g., high-level abstract responses)
• The edge-side Small Language Model (SLM) refines these sketches using the original context
• Enables high-utility responses under strong privacy constraints

Results & Insights

• PRISM achieves up to 3× lower latency and 2.5× lower energy consumption than baselines like Uniform and Selective LDP
• Delivers higher LLM-Judge scores (up to 7.2) under strong privacy budgets
• Outperforms state-of-the-art methods (e.g., Split-and-Denoise, DP-Forward) in terms of both utility and efficiency
• Robust across 8 different model combinations (e.g., GPT-4o + StableLM)

Broader Impact

PRISM enables selective privacy-preserving inference for sensitive domains such as medical, financial, and personal assistants, paving the way for:

• Deploying LLMs responsibly in privacy-critical environments
• Reducing energy costs in cloud-edge infrastructure
• Bridging the tradeoff between privacy and inference quality