IGNN: An Inference Graph Neural Network for Data Sensitivity Assessment Considering Data Inference Leakage in Cyber-Physical Power Grid

Abstract

Modern Cyber-Physical Power Grids (CPPG) rely heavily on fine-grained data exchange, yet they face the concealed threat of Data Inference Leakage (DIL), where attackers infer sensitive parameters from seemingly non-sensitive data. Existing risk assessment methods typically focus on direct cyber-attacks or employ static binary classifications, failing to capture the dynamic risk propagation caused by complex physical and statistical data couplings. To address this, this paper proposes a unified data sensitivity assessment framework based on a novel Inference Graph Neural Network (IGNN). We first construct an inference relation graph that integrates both model-driven and data-driven correlations. An analytic hierarchy process (AHP) model is constructed to calibrate the intrinsic sensitivity baseline. Subsequently, a novel inference-weighted Graph Attention Network (GAT) is developed to quantify the final sensitivity by aggregating the risk propagating through the inference pathways. Experiments on IEEE 30-, 57-bus, ACTIV 200-bus, and case 1354-pegase systems demonstrate that the proposed method effectively quantifies data sensitivity and identifies high-risk data susceptible to DIL, exhibiting strong robustness across different system scales. Quantitative results indicate that the high-sensitivity data identified by our method yield a highly threatening inference accuracy of up to 0.9145, significantly outperforming GCN-based and rule-based benchmarks.