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.

翻译：许多数据挖掘和分析任务依靠网络(图)的抽象化来总结个人之间的关系结构(节点),由于关系数据往往是敏感的,因此我们的目标是寻求有效办法,以产生使用公用保护但隐私保护的结构化数据,在本文件中,我们利用差异隐私框架来制定和执行深层图形生成模型的严格的隐私限制,重点是边缘-DP,以保障个人联系隐私,特别是,我们通过在基于重建的图形生成模型的梯度上注入适当的噪音来实施边缘-DP,同时通过改进结构学习和结构导向图解区分来确保数据实用性。关于两个真实世界网络数据集的广泛实验表明,我们提议的DPGGAN模型能够生成具有有效保护全球结构和严格保护个人联系隐私的图表。