Graph is an important data representation ubiquitously existing in the real world. However, analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph embedding is a powerful tool to solve the graph analytics problem by transforming the graph data into low-dimensional vectors. These vectors could also be shared with third parties to gain additional insights of what is behind the data. While sharing graph embedding is intriguing, the associated privacy risks are unexplored. In this paper, we systematically investigate the information leakage of the graph embedding by mounting three inference attacks. First, we can successfully infer basic graph properties, such as the number of nodes, the number of edges, and graph density, of the target graph with up to 0.89 accuracy. Second, given a subgraph of interest and the graph embedding, we can determine with high confidence that whether the subgraph is contained in the target graph. For instance, we achieve 0.98 attack AUC on the DD dataset. Third, we propose a novel graph reconstruction attack that can reconstruct a graph that has similar graph structural statistics to the target graph. We further propose an effective defense mechanism based on graph embedding perturbation to mitigate the inference attacks without noticeable performance degradation for graph classification tasks. Our code is available at https://github.com/Zhangzhk0819/GNN-Embedding-Leaks.

翻译：图表是真实世界中普遍存在的重要数据代表。 然而, 分析图形数据由于非欧元性质而难以计算。 图像嵌入是一个强大的工具, 可以通过将图形数据转换为低维矢量来解决图形分析问题。 这些矢量也可以与第三方共享, 以获得更多数据背后的洞察力。 虽然共享图嵌入是令人感兴趣的, 相关的隐私风险是尚未探索的。 在本文中, 我们系统地调查通过三次推断攻击嵌入图的信息渗漏。 首先, 我们可以成功推断基本图形属性, 如节点数目、 边缘数和图形密度, 其精确度最高为 0. 89 。 其次, 鉴于关注度和图嵌入的子图, 我们可以非常有信心地确定子图是否包含在目标图表中。 例如, 我们在DD数据集上实现了0. 98 AUC。 第三, 我们提出一个新的图形重建攻击, 以重建一个具有类似图表结构结构统计的图表, 其深度结构统计为GNE 。 我们进一步提议, 以不使用 GNE 递增 的图形 格式, 来降低 GRA 。