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English(EN) Are LLM-Enhanced GNNs Privacy-Safe?

研究发现：增强型GNN存在隐私风险

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文探讨了使用大型语言模型（LLMs）增强图神经网络（GNNs）所带来的隐私影响。该研究引入了一个系统评估隐私风险的框架，发现增强型GNN在提高性能的同时，更容易受到推断链接、标签和成员身份等敏感信息的攻击。研究还评估了差分隐私作为一种防御机制，指出其在缓解风险方面的有效性，但也对其模型效用产生显著影响，凸显了图学习中隐私与性能之间的权衡。 AI

影响 强调了先进图学习系统中潜在的隐私漏洞，促使需要更安全的开发实践。

排序理由 发表在arXiv上的研究论文，详细介绍了增强型GNN的隐私风险。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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研究发现：增强型GNN存在隐私风险

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发表在arXiv上的研究论文，详细介绍了增强型GNN的隐私风险。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Longzhu He, Zelang Wen, Chaozhuo Li, Sen Su ·

    增强型LLM的GNN是否隐私安全？

    arXiv:2608.25727v1 Announce Type: new Abstract: Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving rise to LLM-enhanced GNNs that achieve substantial performance gains. However, thei…

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