arXiv:2609.23193v1 Announce Type: new Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromising task utility remains a major challenge. Existing approaches preserve most of the original semantic content to maintain downstream performance, but this also leaves exploitable cues for reconstructing the original text.
LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication
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