# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
###### Abstract  
Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e.g., discrete locations, search and shopping items) while guaranteeing the confidentiality property known as \(k^{m}\)-anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis. In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments. anonymization; knowledge extraction; ant colony clustering; association rules; utility; privacy; disassociation +
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retail. It does not refer to just one single algorithm or application, but more to a set of applications that can be used to analyze the correlation between data items in order to find relationships between different items and items categories. This study is an investigation of general set-valued data, where data temporality and semantics is not examined. To illustrate the dilemma of data analysis and privacy preservation in set-valued data, let us consider an example of a mobility data, which stores the GPS location of individuals, each record corresponds to the set of visited cities by an individual. Finding a very high association rule between three cities {[Valetta, Bratislava] \(\Rightarrow\) [Salzburg]} might indicate a high demand for mobility solutions between the cities. On the other side, publishing set-valued data raises the problem of privacy preservation. If an individual, [PERSON], visited [Salzburg (Austria), Valetta (Malta), Bratislava (Slovakia), Bergamo (Italy)] and an attacker knows that [PERSON] visited both [Valetta, Bratislava]; he/she can predict with high probability that [PERSON] also visited [Salzburg]. Publishing the dataset unrefined, fails to protect the privacy of [PERSON]'s mobility data. From the above example, we can see that it is necessary to extract knowledge from set-valued datasets but at the same time, the privacy of the individuals should be considered wisely. Publishing raw data is a public disclosure of information collected and puts the privacy of individuals at risk. For that reason, privacy-preserving mechanisms should be applied to the data before publishing it. Anonymization is the process that imposes a level of privacy on the data in an attempt to protect the privacy of individuals participating in the dataset. Anonymization techniques that can be deployed to protect the confidentiality of user data is a wide area of research [3; 4; 5; 6; 7; 8; 9; 10]. Disassociation, is an anonymization technique presented in [2], particularly designed for set-valued data. Basically, it set-valued data are clustered into homogeneous groups (via horizontal partitioning), and then each cluster is split into record chunks (via vertical partitioning) where items of a set are separated into disconnected subsets to preserve the \(k^{m}\)-anonymity privacy constraint. However, the anonymization approach aims are both: to provide a dataset respecting privacy and to make the published data valuable for direct analysis.