论文标题
隐私图形分析：安全生成和联合学习
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
论文作者
论文摘要
由国土安全企业与安全相关的应用程序直接激励，我们专注于对图形数据的隐私分析分析，该分析提供了代表丰富属性和关系的关键能力。特别是，我们讨论了两个方向，即保存隐私图生成和联合图形学习，这可以共同使各个拥有私人图形数据的各方之间的协作。对于每个方向，我们都确定“快速获胜”和“硬问题”。最后，我们演示了一个可以促进模型解释，解释和可视化的用户界面。我们认为，在这些方向上开发的技术将大大提高国土安全企业应对和减轻各种安全风险的能力。
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships. In particular, we discuss two directions, namely privacy-preserving graph generation and federated graph learning, which can jointly enable the collaboration among multiple parties each possessing private graph data. For each direction, we identify both "quick wins" and "hard problems". Towards the end, we demonstrate a user interface that can facilitate model explanation, interpretation, and visualization. We believe that the techniques developed in these directions will significantly enhance the capabilities of the Homeland Security Enterprise to tackle and mitigate the various security risks.
