3 papers
cs.CR2026
SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacy
Shashie Dilhara Batan Arachchige, Robin Carpentier, Hassan Jameel Asghar +1
User prompts provided to large language models (LLMs) may contain private information. One way to protect them is to execute the LLM inside a trusted execution environment (TEE). H…
cs.CR2024
-Privacy for Text and the Curse of Dimensionality
Hassan Jameel Asghar, Robin Carpentier, Benjamin Zi Hao Zhao +1
A widely used method to ensure privacy of unstructured text data is the multidimensional Laplace mechanism for -privacy, which is a relaxation of differential privacy for metr…
cs.CR2024
Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions
Robin Carpentier, Benjamin Zi Hao Zhao, Hassan Jameel Asghar +1
Interactions with online Large Language Models raise privacy issues where providers can gather sensitive information about users and their companies from the prompts. While textual…