LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

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