SpliTEE uses differential privacy for secure LLM inference on trusted hardware

Researchers have developed SpliTEE, a novel architecture designed to enhance the privacy of large language model (LLM) inference on trusted hardware. This system splits LLM computations between a secure, CPU-based trusted execution environment (TEE) and a faster, untrusted GPU. Unlike previous encryption-based methods, SpliTEE employs differential privacy to mask intermediate inputs sent to the GPU, preventing prompt reconstruction attacks. The approach maintains LLMs in the floating-point domain, avoiding quantization and enabling faster inference speeds compared to fully CPU-based or encryption-based split methods, while also achieving higher accuracy. AI

IMPACT Enhances privacy for LLM inference, potentially enabling more sensitive data processing in untrusted environments.

RANK_REASON The item is an academic paper detailing a new method for LLM inference on trusted hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Boneh
• differential privacy
• graphics processing unit
• Llama 3.2:3b
• Qwen3-4B
• Shashie Dilhara Batan Arachchige
• SpliTEE
• Tramer
• Trusted Hardware

• paper
• safety
• infra

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