Differentially Private Weighted Graphs Publication Under Continuous Monitoring

Wen Xu, Zhetao Li, Haolin Liu, Yunjun Gao, Xiaofei Liao, Kenli Li

Abstract: Graph data analysis has been used in various real-world applications to improve services or scientific research, which, however, may expose sensitive personal information. Differential privacy (DP) has become the gold standard for publishing graph data while still protecting personal privacy. However, most existing studies over differentially private graph data publication mainly focus on static unweighted graphs. As interactions between entities in real systems are often dynamically changing and associated with weights, it is desirable to consider the more general scenario of continuous weighted graph publication under DP in the temporal dimension. Therefore, we investigate the problem of publishing weighted graphs satisfying DP under continuous monitoring. Specifically, we consider a server that continuously monitors user data and publishes a sequence of weighted graph snapshots. We propose SwgDP, a novel framework that leverages historical graph data to guide current snapshot generation. SwgDP consists of four key components: node adaptive sampling, dynamic weight optimization, prediction-based community detection and weighted graph generation. We demonstrate that SwgDP satisfies DP, and comprehensive experiments on four real-world datasets and four commonly used graph metrics show that SwgDP can effectively synthesize weighted graph at any time step.