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Diffusion-Based Heterogeneous Graph Synthesis Under Local Differential Privacy

L. Hou,Weiwei Ni,N. Fu,Dongyue Zhang,Ruyu Zhang,Sen Zhang

Published 2025 in IEEE Transactions on Dependable and Secure Computing

ABSTRACT

Many real-world networks can be modeled as decentralized heterogeneous graphs with different types of nodes, each holds a piece of whole network and has its own privacy preference. Local differential privacy (LDP) has been widely used in decentralized graph synthesis to provide privacy guarantees. This paper shows that existing LDP-based graph synthesis approaches are insufficient for preserving topological properties and satisfying the different privacy requirements of heterogeneous nodes when generating synthetic decentralized graphs. To address these problems due to the clueless perturbation and ignorance of the topological properties of existing approaches, we introduce HeG-LDP, a novel privacy-preserving decentralized heterogeneous graph synthesis approach that achieves a considerable utility boost compared to other methods while satisfying the privacy requirements of different types of nodes. Concretely, we propose a heterogeneous graph information extraction approach that exploits the inherent topological nature of graphs to construct background knowledge from the partial nodes in a diffusion manner, which is then used to guide individuals in topological information extraction and perturbation. In addition, to further improve the quality of the generated graph, we propose a dK-series-based graph generation approach, which can optimize the connection probability of node pairs via a delicate combination of degree values and dK-series information, resulting in better maintenance of the topological utility. Comprehensive experiments and theoretical analysis show that our proposed HeG-LDP can yield high-quality synthetic heterogeneous graphs while satisfying edge-LDP.

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