Abstract
Graph data mining has emerged as a prominent area of research in both academic and industrial domains. Dynamic link prediction, a critical subfield within graph data mining, offers a more realistic representation of real-world networks compared to static link prediction, making dynamic link prediction attacks particularly threatening to privacy. While privacy protection in dynamic networks can be achieved by removing certain sensitive links, attackers can still infer hidden sensitive connections from observable network data. Moreover, existing studies seldom address target-level defense against dynamic link prediction attacks. To address these challenges, this paper proposes a Target-Level Privacy protection method against Dynamic Link Prediction attacks (TP-DLP). The method leverages temporal information in dynamic networks to implement targeted protection based on link gradient information, operating within a perturbation range that preserves the inherent characteristics of dynamic networks. Using dynamic social networks as a case study, the approach distinguishes the privacy levels of dynamic links to achieve target-level privacy protection. Extensive experimental results demonstrate that TP-DLP significantly enhances privacy protection while preserving network utility, making it well-suited for targeted defense against dynamic network link prediction. It can be concluded that our method achieves a balanced trade-off between privacy protection effectiveness and network structural fidelity.