  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.
