# Analytics on Anonymity for Privacy Retention in Smart Health Data +
## 4 Evaluation and Experiments
### On Performances  
To demonstrate the effectiveness of our method, we experiment with the public heart disease datasets [31] collected from three different health organizations: Cleveland Clinic Foundation (dataset 1), Hungarian Institute of Cardiology Budapest (dataset2) and V.A. Medical Center, Long Beach, CA (dataset 3). In each data set, we select six most pertinent  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rows**} & \multirow{2}{*}{**Sex**} & \multicolumn{2}{c}{**Alcohol**} & \multirow{2}{*}{**Age**} & \multirow{2}{*}{**Zip**} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**Race**} & \multirow{2}{*}{**Genetic Risk**} & \multirow{2}{*}{**\#Rec.**} \\  & & & & & & & & \\ \hline
1,2 & F & Yes & 35 & 52000 & 143 & Black & No & \(1+3\) \\
3 & M & Yes & 50 & 52000 & 166 & White & No & \(4\) \\
4 & M & Yes & 35 & 52003 & 143 & White & No & \(1\) \\
5,6 & M & Yes & 68 & 52000 & 190 & Hispanic & No & \(3+4\) \\
7,11 & M & Yes & 44 & 52003 & 166 & Asian & Hereditary Thromophilia & \(3+3\) \\
8 & F & Yes & 75 & 52002 & 122 & Native Hawaiian & No & \(3\) \\
9 & F & Yes & 35 & 52003 & 143 & Black & No & \(2\) \\
10 & F & Yes & 75 & 52002 & 143 & White & Hereditary Thromophilia & \(1\) \\
12 & F & Yes & 38 & 52000 & 166 & White & No & \(10\) \\
13,14 & F & Yes & 35 & 52000 & 122 & American Indian & Hereditary Thromophilia & \(1+2\) \\
15 & M & Yes & 38 & 52003 & 122 & White & No & \(3\) \\
16 & F & Yes & 22 & 54004 & 180 & Native Hawaiian & No & \(3\) \\
17 & M & Yes & 20 & 54000 & 180 & American Indian & L.O. Alzheimer’s & \(3\) \\
18 & M & Yes & 21 & 54001 & 180 & Black & Parkinson’s & \(3\) \\
19 & F & Yes & 24 & 54003 & 180 & Asian & Hereditary Thromophilia & \(3\) \\
20 & M & Any & 25 & 54000 & 122 & American Indian & Celiac & \(3\) \\
21 & M & Any & 26 & 54001 & 166 & Asian & L.O. Alzheimer’s & \(3\) \\
22 & F & Yes & 23 & 54003 & 180 & Asian & Parkinson’s & \(3\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Resultant table from Greedy Approach.  
Figure 5: Search Tree for Greedy Approach.  
attributes for our purpose to illustrate privacy protection of our anonymization approach. For the same reason, we also add the Zip attribute for our experiments giving a total of seven attributes. Figure 6 summarizes the attributes of the three data sets with their corresponding attribute values along with each data set size.  
The mechanism to anonymize the data relies on data generalization based on the taxonomy of data of each attribute. Here the taxonomy trees for relevant attributes are shown in Figure 7.  
Note that a combination of the attributes, selected in Figure 6, can be used to re-identify an individual heart patient. Recall that our method aims to quickly find a solution of an anonymized table that satisfies an anonymity requirement and that it maximally preserves the original data. To better understand how our method performs with respect to the trade-off among each criterion (i.e., data preservation, privacy protection, and efficient solution), we compare our method with two other methods that solve a problem focusing on a single criterion.  
Method 1, that is the exhaustive search, aims to find a solution, satisfying anonymity requirements, with maximum information preservation (i.e., retaining the greatest number of data rows), whereas Method 2, that is greedy search, aims to find a solution satisfying anonymity requirements most efficiently. In terms of search, Method 1 exhaustively searches for a solution that has a maximum number of rows in the table, while Method 2 is a greedy search for a table with no rows violating the anonymity requirements. See more details on search algorithms in [30].