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New framework audits and mitigates privacy leakage in cloud-edge LLM collaboration

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to audit and mitigate privacy leakage in cloud-edge collaborative decoding systems. These systems use a small language model on edge devices to process private data and fuse its predictions with a cloud-hosted large language model. The proposed method, called CoVeil, dynamically optimizes transmitted signals to reduce data leakage by up to 87.2% while maintaining collaborative quality, as demonstrated on constructed QA datasets. AI

IMPACT This research could lead to more secure deployment of LLMs for sensitive data processing, enabling wider adoption in privacy-conscious applications.

RANK_REASON This is a research paper published on arXiv detailing a new framework and method for addressing privacy concerns in a specific AI system architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework audits and mitigates privacy leakage in cloud-edge LLM collaboration

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This is a research paper published on arXiv detailing a new framework and method for addressing privacy concerns in a specific AI system architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kejia Zhang, Tianyuan Zou, Zixuan GU, Yang Liu ·

    Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding

    arXiv:2608.29111v1 Announce Type: cross 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 …

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