Privacy-Assured Analytics on Decentralized Graphs:The Case of Graph Learning

Graph learning has garnered increasing attention in recent years, which aims to train machine learning models over graph data to support various graph analytic tasks. Coming with the popularity of graph learning are critical privacy concerns regarding the information-rich graphs in many application domains (e.g., finance, social networks, and healthcare). There is thus an urgent call for privacy-preserving graph learning. In this paper, we target an emerging decentralized graph scenario, where a graph is fully decentralized among a set of nodes in such a way that each node only has a limited local view about the global graph. We propose PDGL, a new system framework that can effectively support privacy-assured model training over a decentralized graph, with privacy protection for the links among the nodes as well as the nodes' private feature data and labels. In contrast to PDGL, prior work does not provide protection for the nodes' links, feature data, and labels simultaneously. Extensive experiments demonstrate that while providing strong privacy protection for decentralized graph data, PDGL can achieve model utility comparable to the baseline setting of centralized graph learning.