Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding

• URL: http://arxiv.org/abs/2608.29111v1
• Date: Sat, 29 Aug 2026 07:40:14 GMT
• Title: Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding
• Abstract summary: We propose a cloud-edge collaborative decoding paradigm, where private data are kept on the edge with a small language model (SLM) producing next-token distributions.<n>We show that such collaboration can expose substantial private-context information.<n>We propose CoVeil, a defense mechanism which dynamically optimize transmitted signals to suppress leakage during decoding time.
• Score: 5.588359981676548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 paradigm with a novel evaluation framework using constructed QA datasets, which show that such collaboration can expose substantial private-context information. To address such privacy leakage, we propose CoVeil, a defense mechanism which dynamically optimizes transmitted signals to suppress leakage during decoding time while preserving the collaborative quality. Extensive evaluations demonstrate that CoVeil consistently improves the privacy-utility trade-off over existing baselines by reducing data leakage by up to 87.2%, with minimal accuracy loss.

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