Differential privacy preservation for graph auto-encoders: A novel anonymous graph publishing model

Li, Xin; Xu, Li; Zhang, Hongyan; Xu, Qikui

doi:10.1016/j.neucom.2022.11.083

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Expanding upon this foundational framework, methods like CoLA [23] and LogLG [13] introduce novel indicators, such as the similarity between positive and negative sample pairs, to identify anomalies. Moreover, based on various graph autoencoders [16,20], methods like AdONE [1] and AnomalyDAE [8] compute the reconstruction errors of nodes, thereby deriving anomaly scores.…”

Section: Detection Based On Graphsmentioning

confidence: 99%

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

Yuan,

Zhou,

Chen

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments

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