# Analytics on Anonymity for Privacy Retention in Smart Health Data +
## 4 Evaluation and Experiments
### On Performances
#### 4.2.2 Comparisons on Varying Anonymity Requirements  
Given a fixed \(k\) with varying shield attributes on the anonymity requirement, the biggest factor to both number of generalizations and total time is the selected shield attributes. The taxonomy trees of the attributes and number of selected attributes both effect the results. Intuitively, the more attributes the shield has the more alternatives for generalization there are. Similarly, if the shield attributes have higher depth of taxonomy trees, there will be more generalizations. As also discussed in [15], to demonstrate that our method still performs well on various shields, we experimented with different shield set sizes and attribute on the same data set, namely dataset 1(Cleveland). The results are shown in Table 7. As shown in the top partition row of Table 7, the anonymity requirement with the greatest number of shield attributes produces the highest number of generalizations in all methods. In the next two partition rows, between the two Anonymity Requirements with three attributes, the Anonymity Requirement (<[Zip, Smoker, Age], 5>) in the third partition row produces a greater number of generalizations than those produced by the Anonymity Requirement (<[Zip, Smoker, Sex], 5>) for all methods. This is as expected because the taxonomy tree of Age is larger than that of Sex. In fact, the size of the taxonomy tree of the shield attribute can influence the number of generalizations more than the number of attributes in the shield. As shown in Table 7, the Anonymity Requirement (<[Zip, Smoker, Sex], 5>) (second partition row) has higher number of attributes than the Anonymity Requirement (<[Zip, Age], 5>) (last partition row) and yet it produces smaller number of generalizations. This is because the size of taxonomy tree of Age is deeper than those of Sex and Smoker. Figure 10: Total Time in Seconds. Figure 9: Number of Rows. In all cases of varying shields on the anonymity requirements, Method 1 (Method 2) generates the highest (lowest) number of generalizations, while ours is in between as it is designed to balance the trade-off between privacy protection (i.e., generalizations) and data preservation (i.e., rows). Our method aims to obtain an anonymized table with maximum information preservation by generating only required amount of generalization. As shown in Table 7, comparing the number of rows of the resulting tables generated by all methods using varying shields on the anonymity requirements, ours and Method 1 generates a maximum number of rows, while results of Method 2 are slightly lower in all but one case. In general, Method 1 finds the anonymity-complied table that has a maximum number of distinct rows by searching through all possible generalizations. Thus, the search is exhaustive and optimal solution (i.e., an anonymity-compliant generalized table with maximum number of rows) is guaranteed. If there are multiple tables with the same number of rows, the first solution found is selected, as it would have the least generalizations (less time). Even though Method 1 generates a solution that retains maximum information preservation, its exhaustive search that requires many generalizations may not be desirable in practice. On the other hand, Method 2 finds an anonymized table by greedily searching for a generalized table that has a minimum number of anonymity violations (i.e., zero). Using a heuristic on the number of violating rows, Method 2 finds a solution without going through all possible generalized tables. Thus, its search is more efficient than Method 1. However, finding the optimal solution (i.e., a generalized table with zero violation) is not guaranteed. Our method combines Methods 1 and 2 by quickly finding a generalized table that has zero anonymity violation as well as being the most informative table (i.e., having maximum number of distinct rows like Method 1). The method is heuristic using the above two evaluation metrics and thus, saves time compared to an exhaustive Method 1.