arXiv:2609.32706v1 Announce Type: new Abstract: Cloud-based large language models (LLMs) require users to disclose plaintext data to service providers, creating privacy risks in sensitive domains. Existing privacy-preserving approaches often trade utility for protection, incur substantial computational or communication overhead, remain vulnerable to reconstruction from intermediate representations, or protect only a subset of the training and inference pipeline.
Learning to Refer: Client-Resolved Generation for Privacy-Aware Language Models
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