论文标题
使用链接差异隐私保护深度图生成
Secure Deep Graph Generation with Link Differential Privacy
论文作者
论文摘要
许多数据挖掘和分析任务依赖网络的抽象（图）来总结个人之间的关系结构（节点）。由于关系数据通常是敏感的，因此我们旨在寻求有效的方法来生成公用事业保留但隐私保护的结构化数据。在本文中，我们利用差异隐私（DP）框架在深度图生成模型上制定和执行严格的隐私限制，重点关注Edge-DP，以确保单个链接隐私。特别是，我们通过将适当的噪声注入基于链接重建的图形生成模型的梯度来实施Edge-DP，同时通过使用面向结构的图形歧视来改善结构学习来确保数据实用程序。在两个现实世界网络数据集上进行的广泛实验表明，我们提出的DPGGAN模型能够生成具有有效保留的全局结构并严格保护的个人链接隐私的图形。
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting proper noise to the gradients of a link reconstruction-based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.
