Anonimos：一种基于LP的加权社会网络图分析方法
Anonimos: An LP based Approach for Anonymizing Weighted Social Network Graphs
摘要：
随着社交网络的日益普及，信息抽取和数据采掘成为一个研究热点。这些社交图谱的分析对于促进发布这些数据集以供外部实体分析是重要的。以前的工作主要集中在节点身份匿名和结构匿名。但随着人们越来越关注将社交网络作为加权网络进行分析，边权重匿名化也变得越来越重要。我们提出了Anónimos，一种基于线性规划的技术，用于匿名化边权重，保留图的线性特性。这些性质形成了许多重要的图论算法的基础，如最短路径问题，k-最近邻，最小成本生成树和最大化信息传播。作为概念证明，我们将Anónimos应用于最短路径问题及其扩展，证明其正确性，分析复杂性，并使用真实的社交网络数据集进行实验评估。我们的实验表明，Anónimos匿名的权重，提高了k-匿名的权重，也打乱了相对排序的权重排序的边缘，从而提供了强大的和有效的匿名的敏感的边缘权重。此外，我们还演示了使用Anónimos生成的不同模型的可组合性，Anónimos是一种允许单个匿名图保留多个线性属性的属性。
英文摘要：
The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present Anónimos, a Linear Programming based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anónimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anónimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. Additionally, we demonstrate the composability of different models generated using Anónimos, a property that allows a single anonymized graph to preserve multiple linear properties.
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