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  1. 首页
  2. Dpar: 分离图形神经网络与节点级差异性隐私
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  2. Dpar: 分离图形神经网络与节点级差异性隐私

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DPAR: 分离图形神经网络与节点级差异性隐私

Qiuchen Zhang1, Hong Kyu Lee1, Jing Ma1

  • 1Emory University, Atlanta, GA, USA.

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|December 23, 2024

在PubMed 上查看摘要

概括
此摘要是机器生成的。

我们介绍了一种用于训练图形神经网络 (GNN) 的新方法,以增强隐私. 我们的方法,分离GNN与差别私有近似个性化PageRank (DPAR),改善了敏感图形数据的隐私-实用性权衡.

关键词:
不同的隐私差异性隐私.图形神经网络的神经网络这就是PageRank.

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科学领域:

  • 机器学习 机器学习
  • 图形分析分析 图形分析
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 图形神经网络 (GNN) 擅长从图形数据中学习.
  • 培训GNN引发了有关敏感节点特征和图形结构的隐私问题.
  • 对于GNN来说,节点级差异隐私 (DP) 具有挑战性,因为在消息传递中节点的敏感性不受限制.

研究的目的:

  • 开发一个节点级别的差异化私有 GNN 培训方法.
  • 为了改善GNN中的隐私-实用性权衡.
  • 为了保护敏感的节点特征和图形结构信息.

主要方法:

  • 提出了一个脱的GNN与差别私有近似个性化PageRank (DPAR).
  • 使用DP PageRank算法解特征投影和消息传递.
  • 利用PageRank识别的top-K邻居进行特征聚合,限制节点灵敏度.

主要成果:

  • 与现有方法相比,实现了更好的隐私-实用性权衡.
  • 通过避免层间传递消息来限制节点的灵敏度.
  • 在等效节点DP级别上证明DPAR的优越实用性.

结论:

  • DPAR为保护隐私的GNN培训提供了一个强大的解决方案.
  • 该方法有效地平衡了隐私保证与模型实用性.
  • DPAR在私人图表表示学习领域取得了进展.