TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network Learning

Jiangli Shao, Yongqing Wang, Fangda Guo, Boshen Shi, Huawei Shen, Xueqi Cheng

Abstract: Privacy concerns on social networks have received extensive attention in recent years. The task of user identity linkage (UIL), which aims to identify corresponding users across different social networks, poses a threat to privacy if applied unethically. Sensitive user information would be inferred with cross-network identity linkages. A feasible solution to this issue is to design an adversarial strategy that degrades the matching performance of UIL models. Nevertheless, most of the current adversarial attacks on graphs are tailored towards models working within a single network, failing to account for the challenges presented by cross-network learning tasks such as UIL. Also, in real-world scenarios, the adversarial strategy against UIL has more constraints as service providers can only add perturbations to their own networks. To tackle these challenges, this paper proposes a novel poisoning strategy to prevent nodes in a target network from being linked to other networks by UIL algorithms. Specifically, the UIL problem is formalized in the kernelized topology consistency perspective, and the objective is formulated as maximizing the structural variations in the target network before and after modifications. To achieve this, a novel graph kernel is defined based on earth mover's distance (EMD) in the edge-embedding space. In terms of efficiency, a fast attack strategy is proposed using greedy searching and a lower bound approximation of EMD. Results on three real-world datasets demonstrate that the proposed method outperforms six baselines and reaches a balance between effectiveness and imperceptibility while being efficient.