Learning the similarity between structured data, especially the graphs, is one of the essential problems. Besides the approach like graph kernels, Gromov-Wasserstein (GW) distance recently draws big attention due to its flexibility to capture both topological and feature characteristics, as well as handling the permutation invariance. However, structured data are widely distributed for different data mining and machine learning applications. With privacy concerns, accessing the decentralized data is limited to either individual clients or different silos. To tackle these issues, we propose a privacy-preserving framework to analyze the GW discrepancy of node embedding learned locally from graph neural networks in a federated flavor, and then explicitly place local differential privacy (LDP) based on Multi-bit Encoder to protect sensitive information. Our experiments show that, with strong privacy protections guaranteed by the $\varepsilon$-LDP algorithm, the proposed framework not only preserves privacy in graph learning but also presents a noised structural metric under GW distance, resulting in comparable and even better performance in classification and clustering tasks. Moreover, we reason the rationale behind the LDP-based GW distance analytically and empirically.
翻译:学习结构化数据,特别是图表之间的相似性是一个基本问题。除了图形内核、Gromov-Wasserstein(GW)距离等方法外,最近还由于具有灵活性,能够捕捉地形特征和特征特征特征,以及处理变异性而引起极大关注。然而,结构化数据被广泛用于不同的数据挖掘和机器学习应用程序。关于隐私问题,访问分散的数据仅限于单个客户或不同的筒仓。为了解决这些问题,我们提议了一个隐私保护框架,以分析从图表神经网络以联合调味的口味在当地学习的结点嵌成的GW差异,然后将本地差异性隐私明确置于多位编码编码的基础上,以保护敏感信息。我们的实验表明,在由美元和瓦雷普西隆-LDP算法保证的强大隐私保护下,拟议框架不仅保护图形学习的隐私,而且还在GW距离下提出一个有明确结构的衡量标准,从而在分类和组合任务方面实现可比的、甚至更好的业绩。此外,我们有理由解释基于LDP的远程分析。