# Data Anonymization for Hiding Personal Tendency in Set-Valued Database Publication
## 5 Experimental Results and Applicability
### Data Utility  
As we have already stated that there is no generic measurement for quantifying data utility. We applied a typical real-world application in set-valued dataset, that is frequent itemset mining. Therefore, we ran FP-Growth algorithm using \(spmf\) data mining open source software [27] to obtain frequent itemset FI over those three original datasets and their anonymized one.  
We determine several support thresholds, ranging from 0.2% to 0.5% to generate FI in \(BMS1\) and \(BMS2\) datasets. However, for the \(WD\) dataset, the support threshold was ranging from 20% to 50% since the dataset has larger number of items and longer tuple length. We evaluated the obtained mining results from the anonymized dataset using our proposed method and that of other methods and compute their similarity to evaluate data accuracy after applying the anonymization methods using the Jaccard similarity measurement in Equation (2).  
Figure 3 shows that the number of FI from all the anonymized datasets was lower than that of the original one. The decrease in the number of FI in the anonymized datasets mainly due to some items being moved or removed from records, as a result, the structure of records in the anonymized database differs from that of the original one. Since the frequent itemset mining depends on the item sequence, consequently, several itemsets that are considered as frequent itemset in original datasets become no longer frequent in the anonymized datasets and vice versa.  
Even so, since our proposed method \(PartSwap\) only moves items from a certain category which has the highest item frequency in a record to another record, it can successfully maintain the items in the anonymized datasets from loss and minimize record structure distortion. Anonymized databases obtained by \(PartSwap\) have a considerably higher number of FI compared with those of \(PartSupp\) and \(SplitPerson\). Even though \(SplitPerson\) does not cause item loss, however, it generates new records in datasets the size of the datasets increases drastically. As the results the number of generated FI under the same support threshold becomes very low. On the other hand, since \(RandSwap\) also uses swapping  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{3}{c}{**Datasets \(\mathcal{D}\)**} \\ \cline{2-4}  & \(BMS1\) & \(BMS2\) & \(WD\) \\ \hline \(|\mathcal{D}|\) & 5000 & 5000 & 797 \\ \(|i\in\mathcal{D}|\) & 12,821 & 23,530 & 174,499 \\ \(|I|\) & 264 & 2717 & 26,506 \\ \(|\overline{|t|}\) & 2.56 & 4.70 & 218.94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of \(\mathcal{D}\).  
strategy to move items from a record to another, it does not cause item loss. As a result, the method also achieves a significantly higher number of FI.  
Figure 4 shows that the method always results in higher similarity results. It is indicating that the method results in lower inaccuracy at a record level compared with others. As a result, the obtained FI from the anonymized datasets \(\widehat{BMS1}\)-\(\mathit{PartSwap}\), \(\widehat{BMS2}\)-\(\mathit{PartSwap}\), \(\widehat{WD}\)-\(\mathit{PartSwap}\) using the proposed method have the highest number of identical frequent itemset to the original one. Note that we can also observe the following results from our simulation. Even though the proposed method yields the highest number of identical FI, it does not always result in the highest number of FI in the anonymized datasets among all considered methods.  
The most prominent feature of our proposed method is that it works very well in a dataset that has a high number of distinct items and a high number of average tuple length. This is because our method selectively determines a pair of record for items swapping and only swap items from a category that has higher item frequency, such that inaccuracy in record level can be minimized. Therefore, we can confirm that due to \(\mathit{PartSwap}\) results in lower inaccuracy, it successfully preserves higher data utility than other methods.