Author

Dali Kaafar

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Preprint Sep 2026

SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacy

This paper demonstrates that prompt reconstruction, even with knowledge of the DP mechanism, cannot recover more information than is contained in an unrelated prompt, and derives an upper bound on the floating-point error from masking and subsequent noise cancellation as a function of the privacy parameter epsilon.

Shashie Dilhara Batan Arachchige, Robin Carpentier, H. Asghar et al. · 0 citations

#artificial intelligence Preprint Sep 2026

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 (TEE), with the gua...

Shashie Dilhara Batan Arachchige, Robin Carpentier, H. Asghar et al. · 0 citations