# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
###### Abstract
A trade-off must be found between privacy preservation and the benefits of knowledge extraction from datasets. Guided by the disassociation technique our investigation started in [1] by studying and improving the probabilistic preservation of associations in a disassociated dataset. After examination of the probabilistic preservation results, we present an optimization of disassociation for a predefined set of associations, we call utility rules. The goal is to save the utility rules from vertical partitioning as much as possible, while respecting the \(k^{m}\)-anonymity privacy constraint of disassociation. A derivative of the ant-based algorithm for the disassociation has been proposed to group data whilst respecting the utility rules. The new problematic in this extension of the work [1], is that in reality, we cannot define every association present in the original dataset as a utility rule in order to optimize its preservation for future analysis. Therefore, the study of association rules in a disassociated dataset cannot be limited to items preserved together without a break up after vertical partitioning. To run an analysis on a disassociated dataset, we must compute a huge set of pre-images of the disassociated dataset, by reconstructing all possible associations between subsets of items disconnected. This new data format, the disassociated dataset, by breaking down a set of items into disconnected subsets of items, makes the analysis over it hard or at least time consuming to achieve. Primarily, it is useless to publish a dataset that cannot be helpful for data analysis. In this extension, the goal is to publish an anonymized set-valued dataset that falls in the neighborhood of the original one, consequently preserving its original format. In this context, the \"neighbor datasets\" terminology seems to fall perfectly to our intention. In an effort to make this article self-sufficient, Section 2 recalls and summarizes our previous work [1] for probabilistic preservation of utility in a disassociated dataset and its implementation as ant clustering. In Section 3, we present our first contribution where we define \"neighbor datasets\" as two similar datasets that fall under a certain radius of distance and present a way to asses the distance between the \"neighbor datasets\" by formalizing the datasets into trees. Our second contribution in this work, presented in Section 4, is a technique that generates a neighbor datasets to the original, from its disassociated result, which can be seen as a statistical-based re-association. We investigate our solution from two perspectives. First, we look at the preservation of association rules which will reflect the synthetic similarity of neighbor datasets. Second, to generalize our approach, we evaluate the distance between the original and reconstructed datasets to have an overview of the created neighborhood using the distance introduced above. Figure 1 summarizes of the whole solution depicted in this article. All experimental results that confirm the applicability of our approach are presented in Section 5. Finally, Section 6 presents conclusive remarks.