Privacy-preserving Graph Analytics: Secure Generation and Federated Learning

TL;DR

This paper explores methods for privacy-preserving graph analysis, focusing on secure graph generation and federated learning to enable collaborative analysis while protecting sensitive data.

Contribution

It introduces new approaches for privacy-preserving graph generation and federated learning, addressing both practical quick wins and challenging problems.

Findings

Proposed a secure graph generation framework

Developed federated graph learning techniques

Created a user interface for model explanation and visualization

Abstract

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.