Privacy-preserving Graph Analytics: Secure Generation and Federated Learning. (arXiv:2207.00048v1 [cs.CR])

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 …

analytics federated learning graph analytics privacy