Graph data is used in a wide range of applications, while analyzing graph data without protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the standard technique of differential privacy to publish a synthetic graph. However, existing differentially private graph synthesis approaches either introduce excessive noise by directly perturbing the adjacency matrix, or suffer significant information loss during the graph encoding process. In this paper, we propose an effective graph synthesis algorithm PrivGraph by exploiting the community information. Concretely, PrivGraph differentially privately partitions the private graph into communities, extracts intra-community and inter-community information, and reconstructs the graph from the extracted graph information. We validate the effectiveness of PrivGraph on six real-world graph datasets and seven commonly used graph metrics.

翻译：图数据被广泛应用于各类场景，但未经保护的图数据分析存在隐私泄露风险。为缓解隐私风险，我们采用标准差分隐私技术发布合成图。然而，现有差分隐私图合成方法要么直接扰动邻接矩阵导致引入过量噪声，要么在图编码过程中损失大量信息。本文提出一种利用社区信息的有效图合成算法PrivGraph。具体而言，PrivGraph通过差分隐私方式将原始图划分为社区，提取社区内与社区间信息，并基于提取的图信息重建图。我们在六个真实图数据集和七种常用图度量指标上验证了PrivGraph的有效性。