Youxiang Zhu, Ning Gao, Xiaohui Liang, and Honggang Zhang Online Large Language Models (LLMs) are widely employed across various tasks, including privacy-sensitive ones like financial advice or paragraph rewriting. Presently, users directly submit prompts to online LLM servers, inadvertently revealing sensitive keywords and facilitating server tracking to build user profiles. In this paper, we proposeContinue reading “Exploiting Privacy Preserving Prompt Techniques for Online Large Language Model Usage (GLOBECOM 2024)”
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