度量空间中微数据的图论链接攻击
A graph theoretic linkage attack on microdata in a metric space
摘要：
某些分析方法需要了解其数据存储在微数据表中的实体之间的空间距离。例如，这种知识对于执行诸如最近邻搜索或聚类之类的数据采掘任务是必要的和足够的。然而，如果为了研究目的，除了微数据之外还公布记录间距离，就必须再次考虑身份泄露的风险。为了解决这个问题，我们引入了一个灵活的图模型的微观数据在度量空间，并提出了一个链接攻击的数据窥探者的背景知识的现实假设的基础上。这种攻击是基于寻找两个顶点标记和边加权图的最大近似公共子图的思想。通过适应算法图论的标准参数，我们的设置，这个任务被转化为相应的产品图中的最大团检测问题。使用一个玩具的例子和模拟数据的实验结果表明，即使是近似的距离公布可能会增加身份泄露的风险不合理。
英文摘要：
Certain methods of analysis require the knowledge of the spatial distances between entities whose data are stored in a microdata table. For instance, such knowledge is necessary and sufficient to perform data mining tasks such as nearest neighbour searches or clustering. However, when inter-record distances are published in addition to the microdata for research purposes, the risk of identity disclosure has to be taken into consideration again. In order to tackle this problem, we introduce a flexible graph model for microdata in a metric space and propose a linkage attack based on realistic assumptions of a data snooper's background knowledge. This attack is based on the idea of finding a maximum approximate common subgraph of two vertex-labelled and edge-weighted graphs. By adapting a standard argument from algorithmic graph theory to our setup, this task is transformed to the maximum clique detection problem in a corresponding product graph. Using a toy example and experimental results on simulated data show that publishing even approximate distances could increase the risk of identity disclosure unreasonably.
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