In cloud computing, there have led to an increase in the capability to store and record personal data (microdata) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create $k$k-anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature.

中文翻译：

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云计算中K-匿名统计公开控制的微聚合排序框架

在云计算中，存储和记录个人数据的能力得到了提高（微观数据) 在云端。在大多数情况下，数据提供者没有/几乎没有控制权，这导致人们担心个人数据可能会被搁浅。微聚合技术寻求以这样一种方式来保护微数据，即可以在不提供任何可以链接到特定个人的私人信息的情况下发布和挖掘数据。最佳的微聚合方法必须最大限度地减少由这种替换过程造成的信息丢失。挑战在于如何最大限度地减少微聚合过程中的信息丢失。本文提出了一种用于统计披露控制 (SDC) 的排序框架，以保护云计算中的微数据。它由两个阶段组成。在第一阶段，一种算法以特定方式对数据集中的所有记录进行排序，以确保在微聚合期间，非常不同的观察结果永远不会进入同一个集群。在第二阶段，使用微聚集方法来创建$千$克-匿名集群，同时最大限度地减少信息丢失。将所提出的技术的性能与最新的微聚集方法进行了比较。使用基准数据集的实验结果表明，所提出的算法的性能明显优于文献中现有的关联技术。