arXiv (OAI Expanded)
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SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing
User prompts provided to large language models (LLMs) may contain sensitive or private information that can be misused by remotely deployed models, such as through inadvertent memorization during retraining. One way to protect user prompts is to execute the LLM inside a trusted execution environment
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