Disassociation is a bucketization based anonymization technique that divides a set-valued dataset into several clusters to hide the link between individuals and their complete set of items. It increases the utility of the anonymized dataset, but on the other side, it raises many privacy concerns, one in particular, is when the items are tightly coupled to form what is called, a cover problem. In this paper, we present safe disassociation, a technique that relies on partial suppression, to overcome the aforementioned privacy breach encountered when disassociating set-valued datasets. Safe disassociation allows the km-anonymity privacy constraint to be extended to a bucketized dataset and copes with the cover problem. We describe our algorithm that achieves the safe disassociation and we provide a set of experiments to demonstrate its efficiency.

中文翻译：

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设置值数据集的安全分离

Disassociation 是一种基于桶化的匿名化技术，它将集合值数据集划分为几个集群，以隐藏个人与其完整项目集之间的联系。它增加了匿名数据集的效用，但另一方面，它引发了许多隐私问题，特别是当项目紧密耦合形成所谓的覆盖问题时。在本文中，我们提出了安全分离，一种依赖于部分抑制的技术，以克服在分离设置值数据集时遇到的上述隐私泄露问题。安全分离允许将公里匿名隐私约束扩展到桶化数据集并处理覆盖问题。我们描述了实现安全分离的算法，并提供了一组实验来证明其效率。