Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding
Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private data. Yet powerful LLMs typically cannot be deployed on the resource-constrained devices where private data resides, and uploading private data to cloud-hosted LLMs exposes sensitive information. Recent work addresses this tension with a cloud-edge collaborative decoding paradigm, where private data are kept on the edge with a small language model (SLM) producing next-token distributions, which are fused with predictions from a cloud LLM operating solely on public data. In this paper, we systematically analyze the privacy risks of such a para...
| Authors | Kejia Zhang, Tianyuan Zou, Zixuan GU et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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