# Analytics on Anonymity for Privacy Retention in Smart Health Data +
## 4 Evaluation and Experiments
### On Performances
#### 4.2.1 Comparisons on Single Shield  
Shield attributes in this experiment are Age, Sex, Smoker and Zip and a given anonymity requirement is <[Zip, Smoker, Sex, Age], 5>. We evaluate in terms of three metrics: number of generalizations, number of table rows, and time. For the number of generalizations, we measure the total number of generalizations applied during the search  
Figure 6: Summary of the Three Data Sets.  
Figure 7: Taxonomy Trees.  
for a solution. It indicates the degree of privacy protection. The more generalizations we use, the table becomes more anonymous (but less data preservation). Each generalization transforms a table into a new table. However, when generalizing a table on multiple attributes, the order of the attribute applied for generalization does not affect the resulting table. For example, generalizing a table on attribute _age_ then generalizing the resulting table on attribute Sex gives the same table as first generalizing a given table on Sex then generalizing the resulting table on attribute Age. Hence, we label the tables that have the same generalizations as duplicate and only keep one of the tables. The experiment results on total number of generalizations are shown in Figure 8.  
As seen in Figure 8, although each method finds a solution that satisfies anonymity requirements, Method 2 uses optimal number of generalizations in all of the three data sets. This is as expected because the number of generalization steps effect how quickly we can find the solution. On the other hand, Method 1 has the highest number of generalizations in all the three data sets as expected. This can be explained by the fact that Method 1 aims to maximize the data information and thus, it searches over all possible generalized tables for the best solution giving the highest number of rows. On the other hand, the results for our method are in between because it is a trade-off solution that compromises among the three criteria.  
The second metric is the number of (distinct) rows that the solution table has. As shown in Figure 6, initially data sets 1-3 has 303, 294 and 200 rows, respectively. Number of rows measures the quality of the result in terms of information preservation. The more distinct row the table has the more original information is preserved. The comparison results are shown in Figure 9. As shown in Figure 9, our method and Method 1 produce the solutions with the same number of rows in all the three data sets. In fact, both obtained an anonymity-complied solution with optimal number of rows. However, as observed in Figure 8, our method uses less effort in terms of the number of generalizations applied. This favors our method in that it takes less work (i.e., number of generalizations) and yet it retains optimal information (i.e., number of rows). The third metric is time that each method takes to find its anonymity-complied solution. Figure 10 shows the comparison results.  
As expected, Method 2 has the minimum time as its design (since Method 2 greedily searches for the solution and returns once it finds a solution, see Section 4.1) and Method 1 has the maximum time in finding the solution in all the three data sets. This is because the time is associated with the effort in generalization and thus, the number of generalizations. On the other hand, our method gives a compromised solution in that it is relative fast to find a solution and also retains the high number of data rows.  
Figure 8: Total Number of Generalizations.