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：图数据广泛应用于各类应用场景中，然而未经保护的图数据分析易引发隐私泄露风险。为降低隐私风险，我们采用差分隐私标准技术发布合成图。然而，现有差分隐私图合成方法要么通过直接扰动邻接矩阵引入过多噪声，要么在图编码过程中造成显著信息损失。本文提出一种名为PrivGraph的高效图合成算法，通过利用社区信息实现隐私保护。具体而言，PrivGraph以差分隐私方式将原始图划分为社区，提取社区内及社区间信息，并基于提取的图信息重构图。我们通过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。