论文标题
图形机学习中的隐私和透明度：统一的观点
Privacy and Transparency in Graph Machine Learning: A Unified Perspective
论文作者
论文摘要
Graph Machine Learning（GraphMl）将经典的机器学习推广到不规则的图形域，它享有最近的复兴，导致了令人眼花and乱的模型及其在多个域中的应用。随着政府机构对可信赖的AI系统的敏感领域和法规的日益适用性，研究人员已经开始研究透明度和图形学习的隐私问题。 但是，这些主题主要是独立研究的。在该立场论文中，我们提供了关于GraphMl隐私和透明度相互作用的统一观点。特别是，我们描述了对GraphMl中隐私 - 透明度权衡的正式调查的挑战和可能的研究方向。
Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by governmental agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML. In particular, we describe the challenges and possible research directions for a formal investigation of privacy-transparency tradeoffs in GraphML.
