Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly sharing the streaming graphs poses significant privacy risks. Differential privacy, offering strict theoretical guarantees, has emerged as a standard approach for private graph data synthesis. However, existing methods predominantly focus on static graph publishing, neglecting the intrinsic relationship between adjacent graphs, thereby resulting in limited performance in streaming data publishing scenarios. To address this gap, we propose PSGraph, the first differentially private streaming graph synthesis framework that integrates temporal dynamics. PSGraph adaptively adjusts the privacy budget allocation mechanism by analyzing the variations in the current graph compared to the previous one for conserving the privacy budget. Moreover, PSGraph aggregates information across various timestamps and adopts crucial post-processing techniques to enhance the synthetic streaming graphs. We conduct extensive experiments on four real-world datasets under five commonly used metrics. The experimental results demonstrate the superiority of PSGraph.

翻译：流图在日常生活中无处不在，例如不断演化的社交网络和动态通信系统。由于图中包含敏感信息，直接共享流图会带来显著的隐私风险。差分隐私因其提供严格的理论保证，已成为私有图数据合成的标准方法。然而，现有方法主要集中于静态图发布，忽略了相邻图之间的内在联系，导致在流数据发布场景中性能受限。为弥补这一不足，我们提出了PSGraph，首个融合时序动态的差分隐私流图合成框架。PSGraph通过分析当前图相较于前序图的变化，自适应调整隐私预算分配机制以节约隐私预算。此外，PSGraph聚合不同时间戳的信息，并采用关键的后处理技术以提升合成流图的质量。我们在四个真实数据集上基于五种常用指标进行了大量实验。实验结果证明了PSGraph的优越性。