Graph matching over two given graphs is a well-established method for re-identifying obscured node labels within an anonymous graph by matching the corresponding nodes in a reference graph. This paper studies a new application, termed the graph-signal-to-graph matching (GS2GM) problem, where the attacker observes a set of filtered graph signals originating from a hidden graph. These signals are generated through an unknown graph filter activated by certain input excitation signals. Our goal is to match their components to a labeled reference graph to reveal the labels of asymmetric nodes in this unknown graph, where the excitations can be either known or unknown to the attacker. To this end, we integrate the existing blind graph matching algorithm with techniques of graph filter inference and covariance-based eigenvector estimation. Furthermore, we establish sufficient conditions for perfect node de-anonymization through graph signals, showing that graph signals can leak substantial private information on the concealed labels of the underlying graph. Experimental results validate our theoretical insights and demonstrate that the proposed attack effectively reveals many of the hidden labels, particularly when the graph signals are adequately uncorrelated and sampled.

中文翻译：

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用于网络去匿名化攻击的 Graph-Signal-to-Graph 匹配


在两个给定图形上进行图形匹配是一种行之有效的方法，通过匹配参考图中的相应节点来重新识别匿名图形中被遮挡的节点标签。本文研究了一种称为图信号到图匹配 （GS2GM） 问题的新应用，其中攻击者观察到一组来自隐藏图的过滤图信号。这些信号是通过由某些输入激励信号激活的未知图形滤波器生成的。我们的目标是将它们的分量与标记的参考图相匹配，以揭示这个未知图中不对称节点的标签，其中激励可能是攻击者已知的，也可能是未知的。为此，我们将现有的盲图匹配算法与图滤波器推理和基于协方差的特征向量估计技术相结合。此外，我们通过图信号为完美的节点去匿名化建立了足够的条件，表明图信号可以泄露底层图的隐藏标签上的大量私人信息。实验结果验证了我们的理论见解，并表明所提出的攻击有效地揭示了许多隐藏的标签，特别是当图信号充分不相关和采样时。