约束对基于聚类和修改的图匿名化方法的信息丢失和风险的影响
The effect of constraints on information loss and risk for clustering and modification based graph anonymization methods
摘要：
在本文中，我们提出了一种新的方法匿名在线社交网络图，可以与现有的扰动方法，如聚类和修改。本文的主要观点是，通过施加额外的约束，可以选择哪些节点，我们可以减少信息损失的关键结构指标，同时保持可接受的风险。我们提出并评估了两个约束条件，“本地1”和“本地2”，选择最相似的子图在同一个社区，而不包括一些关键的结构节点。为此，我们引入了一种新的距离度量的基础上，局部子图的特点，并使用同构匹配器进行校准。实证测试进行了三个真正的OSN数据集，六个信息损失的措施，五个对手的查询作为风险措施，和不同水平的k-匿名。结果表明，总体而言，有约束的方法给出了最好的结果，信息损失和披露风险。
英文摘要：
In this paper we present a novel approach for anonymizing Online Social Network graphs which can be used in conjunction with existing perturbation approaches such as clustering and modification. The main insight of this paper is that by imposing additional constraints on which nodes can be selected we can reduce the information loss with respect to key structural metrics, while maintaining an acceptable risk. We present and evaluate two constraints, 'local1' and 'local2' which select the most similar subgraphs within the same community while excluding some key structural nodes. To this end, we introduce a novel distance metric based on local subgraph characteristics and which is calibrated using an isomorphism matcher. Empirical testing is conducted with three real OSN datasets, six information loss measures, five adversary queries as risk measures, and different levels of k-anonymity. The results show that overall, the methods with constraints give the best results for information loss and risk of disclosure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
