# Data Anonymization for Hiding Personal Tendency in Set-Valued Database Publication
## 2 Related Work
### Swapping Techniques  
Data swapping techniques have been widely used for statistical disclosure control in micro dataset publishing. Even though there is a dispute of its side effect, i.e., the techniques induce information inaccuracy at a record level due to some values of records are swapped to another record. However, such techniques can preserve items in a set-valued database from loss. As a result, data recipients may obtain all information of the items even if the database has been anonymized. In addition, in a set-valued database, the side effect can also bring advantage for certain application like recommender system. When items of an individual are swapped with items from another individual, there is a chance for the individuals to view different things in his/her recommendation list, as a result, the recommender system can increase its serendipity. In recommender system serendipity, it is important to overcome overspecialization problem and broaden user preferences [14].  
The pioneering work on data anonymization using swapping technique [9; 13] has successfully implemented it to micro datasets for anonymizing numerical and categorical attributes. Another popular swapping method to protect privacy in a micro dataset database is called rank swapping. The method firstly sorts all the values of attribute \(X\) in ascending order, then each value \(v\in X\) is swapped randomly with other value \(v^{\prime}\in X\) under the defined range \(p\%\) of the number \(v\). It is stated in [15] that rank swapping results in better statistical data utility than ordinary data swapping.  
Motivated to improve rank swapping, two protection schemes namely rank swapping p-distribution and rank swapping p-buckets are proposed in [16]. The former combines normal probability distribution of the database with defined \(p\%\) value to swap value \(v\in X\) with other value \(v^{\prime}\in X\). On the other hand, rank swapping p-buckets splits the sorted values of an attribute 'X' into several \(p\) buckets. It firstly selects a bucket \(B_{s}\) based on a certain probability function and then the values in \(B_{s}\) are swapped randomly and uniformly.  
Those previously mentioned swapping methods work in micro datasets where in general it only contains a small number of data attributes and therefore it cannot be directly implemented in set-valued databases which usually have a large number of attributes. Moreover, the existing rank swapping techniques rely on randomization to swap the values which cause severe distortion record structure in set-valued databases.  
A more recent swapping technique has been implemented to avoid negative association rules [17]. It firstly creates databases to satisfy \(\ell\)-diversity [18]. Once the databases satisfy that, the next step is to find all records containing negative association rules and put the records in set \(Y\). Sensitive values in negative association rules that are not compatible are swapped to another record that is compatible with the sensitive attribute in set \(C\). These steps are recursively run until there is no intersection between \(Y\) and \(C\).