近似最短距离查询与高级图分析在大规模加密图

Yuchuan Luo, Dongsheng Wang, Shaojing Fu, Ming Xu, Yingwen Chen, Kai Huang
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引用次数: 0

摘要

了解图的特征对图分析非常重要。在众多属性中,最短距离是最基本的,也是应用最广泛的属性。随着云计算的出现,数据所有者自然会选择将其海量图形托管在云上,并将最短距离的查询服务外包给云。然而,新范式带来了严重的安全问题,因为图数据和最短距离查询可能包含数据所有者和用户的敏感信息。在本文中,我们提出了一种新的方案来支持在大规模加密图上使用高级图分析来保护隐私的近似最短距离查询,该方案使不可信云能够在不知道查询内容和外包图的敏感信息的情况下回答最短距离查询和高级图度量(例如,节点中心性)。与最先进的解决方案相比,我们的设计不仅可以支持高效准确的最短距离近似,还可以支持先进的图形分析。我们证明了我们的方案在选择明文模型下是安全的。在实际数据集上的实验结果表明,该方法具有较高的逼近精度和实用效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximate Shortest Distance Queries with Advanced Graph Analytics over Large-scale Encrypted Graphs
Understanding graph characteristics is of great importance for graph analytics. Among the many properties, shortest path distance is the fundamental and widely used one. With the advent of cloud computing, it is a natural choice for the data owners to host their massive graphs on the cloud and outsource the shortest distance querying service to it. However, the new paradigm brings serious security concerns as graph data and shortest distance queries may contain sensitive information of data owners and users. In this paper, we propose a novel scheme to support privacy-preserving approximate shortest distance queries with advanced graph analytics over large-scale encrypted graphs, which enables an untrusted cloud to answer shortest distance queries as well as advanced graph metrics (e.g., node centrality) without knowing the content of queries and the sensitive information of outsourced graphs. Compared with the state-of-the-art solutions, our design can support not only efficient and accurate shortest distance approximation, but also advanced graph analytics. We prove that our scheme is secure under the chosen-plaintext model. Experimental results over real-world datasets show that our scheme achieves high approximation accuracy with practical efficiency.
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