A privacy protection method for large-scale graph data supported by homomorphic encryption
Given the characteristics of large-scale graph data, such as high-dimensional sparsity, topological coupling perturbations, and inherent heterogeneity in sensitive contexts, traditional coarse-grained privacy identification rules are unable to characterize these non-orthogonal semantic features. This leads to technical...