Local Differential Private Spatio- Temporal Dynamic Graph Learning for Wireless Social Networks

Differential-Private Graph Neural Networks (DP-GNNs) have generated remarkable research results, enabling them to effectively tackle the privacy leakage problem in graph learning. However, most DP-GNNs do not consider the temporal-dimensional scenarios. In tasks involving spatiotemporal graph training, such as wireless social networks analysis, the sensitive interactive information in each time graph should be protected. Therefore, we propose spatio-temporal dynamic graph learning with enhanced local differential privacy (LDP-STG). First, we design a weighted graph perturbation encoder based on the Bernoulli distribution and Laplace mechanism, which protects the structures of weighted graphs in time series under edge-level local differential privacy. Second, we employ an attention mechanism to learn dynamic graph node embedding from spatial and temporal dimensions. The theoretical analysis proves that our LDP-STG realizes differential privacy guarantees. We conduct experiments mainly on two communication datasets (i.e., Enron and UCI), which shows that our LDP-STG can achieve better privacy utility tradeoffs compared with traditional mechanisms in terms of snatiotemnoral dimensions.