Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, financial transaction analysis across multiple banks, and analyzing the transmission of infectious diseases across multiple hospitals. We define the federated graph analytics, a new problem for collaborative graph analytics under differential privacy. Although differentially private graph analysis has been widely studied, it fails to achieve a good tradeoff between utility and privacy in federated scenarios, due to the limited view of local clients and overlapping information across multiple subgraphs. Motivated by this, we first propose a federated graph analytic framework, named FEAT, which enables arbitrary downstream common graph statistics while preserving individual privacy. Furthermore, we introduce an optimized framework based on our proposed degree-based partition algorithm, called FEAT+, which improves the overall utility by leveraging the true local subgraphs. Finally, extensive experiments demonstrate that our FEAT and FEAT+ significantly outperform the baseline approach by approximately one and four orders of magnitude, respectively.

翻译：跨多个机构的协作式图分析正变得越来越普遍。现实案例包括跨不同社交平台的社交网络分析、跨多家银行的金融交易分析，以及跨多家医院的传染病传播分析。我们定义了联邦图分析这一新问题，即在差分隐私约束下进行协作式图分析。尽管差分隐私图分析已得到广泛研究，但由于本地客户端视图有限且多个子图间存在信息重叠，在联邦场景下难以实现效用与隐私的良好权衡。受此启发，我们首先提出一个名为FEAT的联邦图分析框架，该框架能够在保护个体隐私的同时支持任意的下游通用图统计任务。此外，我们基于所提出的基于度的划分算法，引入了优化框架FEAT+，该框架通过利用真实的本地子图来提升整体效用。最后，大量实验表明，我们的FEAT和FEAT+分别以约一个和四个数量级的优势显著优于基线方法。