提出首个保障节点级差分隐私的图释放方法，能保持网络结构特性。
GRAND: Graph Release with Assured Node Differential Privacy
- 基于潜在空间模型，在保证节点隐私的前提下生成新图
- 释放后的图在渐近意义上与原图分布一致
- 适合需要保护隐私又保留网络结构的研究者使用
差分隐私是保护数据敏感信息的成熟框架，但其在图数据尤其是节点层面的应用仍不充分。现有方法要么仅限于查询式输出预设网络统计量，要么无法保持网络关键结构特征。本文提出GRAND（Graph Release with Assured Node Differential Privacy），据我们所知，这是首个在确保节点级差分隐私的同时释放完整网络并保留结构特性的机制。在广泛的潜在空间模型下，我们证明释放后的网络渐近服从与原始网络相同的分布。通过在合成与真实世界数据集上的大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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