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 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.

翻译：差分隐私是保护数据中敏感信息的成熟框架。尽管已在多个领域广泛应用，但其在网络数据——特别是节点层面——的应用仍显不足。现有节点级隐私保护方法要么仅关注基于查询的方式（将输出限制为预设的网络统计量），要么无法保持网络的关键结构特性。本研究提出GRAND（基于节点差分隐私保证的图数据发布方法），据我们所知，这是首个在确保节点级差分隐私的同时保持网络结构特性的网络发布机制。在广泛的隐空间模型类别下，我们证明所发布的网络渐近服从与原网络相同的分布。通过合成数据集和真实数据集的广泛实验，验证了该方法的有效性。