Propagation Mechanisms of Security Risks in Public Data Circulation via Inter-Risk Dependencies: A DSM-Based Network Analysis.

The rapid expansion of public data circulation has introduced complex, interdependent security risks that traditional, isolated governance approaches often fail to mitigate. Drawing on sociotechnical systems theory, systemic risk theory, and network theory, this study proposes a quantitative network analysis approach to identify, model, and evaluate the propagation of these systemic risks. By employing a methodological triangulation strategy integrating a systematic literature review, documented real-world incidents, structured expert consultation, and a survey of 102 field practitioners, we identified 12 core risk factors across the institutional, ethical, technological, and data domains. Using the design structure matrix (DSM) method, we constructed a directional dependency matrix to model the public data circulation security risk network. Topological analysis reveals clear structural differentiation and cross-domain transmission patterns. The institutional-data interface contains the largest combined number of links, while several other cross-domain interfaces also show substantial connectivity. Betweenness centrality analysis further identifies key nodes and structurally important transmission links, providing a structural basis for prioritizing subsequent intervention design and empirical evaluation. Overall, this study provides a systems-level understanding of how public data circulation security risks propagate and offers theoretical and methodological insights for improving data security governance.