Graph neural networks (GNNs) are designed to use attributed graphs to learn representations. Such representations are beneficial in the unsupervised learning of clusters and community detection. Nonetheless, such inference may reveal sensitive groups, clustered systems, or collective behaviors, raising concerns regarding group-level privacy. Community attribution in social and critical infrastructure networks, for example, can expose coordinated asset groups, operational hierarchies, and system dependencies that could be used for profiling or intelligence gathering. We study a defensive setting in which a data publisher (defender) seeks to conceal a community of interest while making limited, utility-aware changes in the network. Our analysis indicates that community concealment is strongly influenced by two quantifiable factors: connectivity at the community boundary and feature similarity between the protected community and adjacent communities. Informed by these findings, we present a perturbation strategy that rewires a set of selected edges and modifies node features to reduce the distinctiveness leveraged by GNN message passing. The proposed method outperforms DICE in our experiments on synthetic benchmarks and real network graphs under identical perturbation budgets. Overall, it achieves median relative concealment improvements of approximately 20-45% across the evaluated settings. These findings demonstrate a mitigation strategy against GNN-based community learning and highlight group-level privacy risks intrinsic to graph learning.

翻译：图神经网络（GNN）旨在利用属性图学习节点表示。此类表示在无监督聚类与社区检测任务中具有显著优势。然而，这种推断可能暴露敏感群体、聚类系统或集体行为，从而引发群体层面隐私保护的担忧。例如，社交网络与关键基础设施网络中的社区属性可能揭示协调的资产组、运营层级及系统依赖关系，这些信息可能被用于用户画像或情报收集。本研究探讨一种防御场景：数据发布方（防御者）期望在保持网络可用性的前提下，通过有限度的网络修改来隐藏目标社区。分析表明，社区隐藏效果主要受两个可量化因素影响：社区边界的连接密度，以及受保护社区与相邻社区间的特征相似性。基于此发现，我们提出一种扰动策略，通过重连选定边集并修改节点特征，以降低GNN消息传递机制所利用的社区区分度。在相同扰动预算下，该方法在合成基准数据集与真实网络图上的实验表现均优于DICE方法。总体而言，在评估的所有场景中，该方法实现了约20-45%的中位数相对隐藏提升。这些结果表明了针对基于GNN的社区学习行为的有效防御策略，同时揭示了图学习技术固有的群体层面隐私风险。