PrivGraph：利用社区信息进行差分隐私图数据发布
PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information
AI总结：
针对图数据发布中的隐私风险，提出利用社区信息的差分隐私图合成算法PrivGraph，通过社区划分与信息提取重建图，在六个真实数据集和七个指标上验证有效性。
摘要：
图数据被广泛应用于各类场景，然而在无保护措施的情况下分析图数据容易引发隐私泄露风险。为降低隐私风险，我们采用标准的差分隐私技术来发布合成图。然而，现有的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遭受显著的信息损失。本文提出了一种有效的图合成算法PrivGraph，通过利用社区信息来实现。具体而言，PrivGraph以差分隐私方式将私有图划分为社区，提取社区内和社区间的信息，并从提取的图信息中重建图。我们在六个真实世界图数据集和七个常用图指标上验证了PrivGraph的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
