C2P-M: Critical Connection Protection in Multiplex Graphs

Conggai Li, Wei Ni, Ming Ding, Youyang Qu, Jianjun Chen, Wenjie Zhang, Thierry Rakotoarivelo

Abstract: Multiplex graphs represent diverse real-world interactions among entities, where multiple relationship types coexist within the same set of entities. These graphs introduce privacy risks, as data collectors can exploit cross-layer dependencies to infer hidden and sensitive connections. In this work, we propose a C2P-M framework that identifies and protects critical connections while preserving the structural information in multiplex graphs. Unlike conventional methods for single-layer graphs that perturb all edges uniformly, C2P-M selectively protects critical connections, maintaining the analytical usability of the graph. To achieve this, we introduce the multiplex $p$-cohesion model, which incorporates new score functions that account for both intra-layer and inter-layer dependencies, enabling precise identification of critical connections for each vertex. For privacy protection, our method protects the identified critical connections, leveraging an adaptive Randomized Response (RR) mechanism to ensure $\varepsilon$-Local Differential Privacy (LDP). We formally prove that C2P-M satisfies $\varepsilon$-LDP. Extensive experiments on eight real-world multiplex graph datasets demonstrate that C2P-M significantly outperforms baseline privacy-preserving methods, achieving a better privacy-utility trade-off.

External IDs:doi:10.1109/tkde.2026.3655741