标题： GRAND：具有保证节点差分隐私的图释放 显示英文标题

标题： GRAND: Graph Release with Assured Node Differential Privacy

摘要： 差分隐私是一个用于保护数据中敏感信息的成熟框架。 尽管在各个领域得到了广泛应用，但其在网络数据中的应用——特别是节点级别的应用——仍研究不足。 现有的节点级别隐私方法要么仅关注基于查询的方法，这些方法将输出限制为预定义的网络统计信息，要么无法保留网络的关键结构特性。 在本工作中，我们提出了GRAND（具有保证节点差分隐私的图释放），据我们所知，这是第一个在网络释放过程中确保节点级别差分隐私并保留结构特性的网络释放机制。 在一大类潜在空间模型下，我们证明了释放的网络渐近地遵循与原始网络相同的分布。 通过在合成和真实世界数据集上的大量实验评估了该方法的有效性。 显示英文摘要

摘要： Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data -- particularly at the node level -- remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based approaches, which restrict output to pre-specified network statistics, or fail to preserve key structural properties of the network. In this work, we propose GRAND (Graph Release with Assured Node Differential privacy), which is, to the best of our knowledge, the first network release mechanism that releases entire networks while ensuring node-level differential privacy and preserving structural properties. Under a broad class of latent space models, we show that the released network asymptotically follows the same distribution as the original network. The effectiveness of the approach is evaluated through extensive experiments on both synthetic and real-world datasets.