With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

中文翻译：

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多子图模式匹配模型的有效框架

随着在云中存储大数据图的流行，远程云上子图模式匹配的出现受到了启发。通常，子图模式匹配是根据子图同构定义的，这是一个 NP 完全问题，有时过于严格，无法在某些应用中找到有用的匹配。而如何在子图模式匹配中保护数据图的隐私又不破坏匹配结果是一个重要的问题。因此，我们提出了一种新颖的框架来实现云中的隐私保护子图模式匹配。为了保护数据图中的结构隐私，我们首先开发了一种基于 k-自同构模型的方法。此外，我们使用基于成本模型的标签泛化方法来保护数据图和模式图中的标签隐私。在k-自守图的生成过程中，可能会在原始数据图中引入大量噪声边或顶点。因此，我们使用外包图（它只是 k-自守图的一个子集）来回答子图模式匹配。通过这种方式可以大大提高模式匹配过程的效率。对真实世界数据集的大量实验证明了我们框架的高效率。