# Analytics on Anonymity for Privacy Retention in Smart Health Data +
## 3 Proposed Multi-Faceted Anonymity Analytics Approach  
This section describes the proposed approach that analyzes anonymity in multiple aspects to protect data owner's privacy. Figure 1 shows a general overview of the approach. As shown in the figure, the approach identifies the attributes susceptible to information leakages in pre-analytics process. The user can choose to increase anonymity of the vulnerable attributes before the anonymization procedure or directly anonymizes the data using the findings of the vulnerable attributes as guidance. The user can also choose to anonymize the smart health dataset, without applying pre-analytics. Then, the data are anonymized, by our IAB (Intelligent Anonymity Balance) anonymization technique that produces data satisfying a given anonymity requirement while optimizing data retention. The anonymized data are then analyzed in the post-analytics process to see if the critical attributes in the resulting anonymized data are vulnerable to information leakage (e.g., via inference of attackers). If they are further actions can be taken (e.g., alerting data publishers or injecting additional \"fake\" data to increase indistinguishability of the vulnerable individuals). The first two steps are described in this section whereas the post-analytics step will be described in Section 5. For easy referencing, since we will describe and illustrate each section with the same data, we briefly introduce them along with common terms and notations below.  
Given a (data) table \(T\) (or relational database) with \(A\), a set of attributes \(A_{1}\), \(A_{2}\),..., \(A_{n}\), a data _record_ represents an instance of a tuple \((a_{1}\), \(a_{2}\),..., \(a_{n})\), where data entry \(a_{i}\in dom(A_{i})\), a set of all possible values of \(A_{i}\). Consider Table 1, each row represents a unique tuple of attribute values where the last column represents the number of records for each row. Here Row 2 represents a unique tuple (\(F\), Low, 35, 52000, 143, Black, No) with three instances of records. As shown in Table 1, Rows 1, 4, 10, 13 are obviously vulnerable to privacy threats since each has one record instance giving low anonymity and easy for re-identification. Next, we will describe the analytical approach.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rows** & **Sex** & **Alcohol Cons.** & **Age** & **Zip** & **Weight** & **Race** & **Genetic Risk** & **\#Rec.** \\ \hline
1 & F & Med & 35 & 52000 & 143 & Black & No & 1 \\
2 & F & Low & 35 & 52000 & 143 & Black & No & 3 \\
3 & M & Med & 50 & 53000 & 166 & White & No & 4 \\
4 & M & Low & 35 & 52003 & 143 & White & No & 1 \\
5 & M & Med & 68 & 52000 & 190 & Hispanic & No & 3 \\
6 & M & High & 68 & 52000 & 190 & Hispanic & No & 4 \\
8 & F & Low & 75 & 52002 & 122 & Native Hawaiian & No & 4 \\
9 & F & Med & 35 & 52003 & 143 & Black & No & 2 \\
10 & F & High & 75 & 52002 & 143 & White & Hereditary Thromophilia & 1 \\
11 & M & High & 44 & 52003 & 166 & Asian & Hereditary Thromophilia & 3 \\
12 & F & Med & 38 & 52000 & 166 & White & No & 4 \\
13 & F & High & 35 & 52000 & 122 & American Indian & Hereditary Thromophilia & 1 \\
14 & F & Low & 35 & 52000 & 122 & American Indian & Hereditary Thromophilia & 2 \\
15 & M & Med & 38 & 52003 & 122 & White & No & 3 \\
16 & F & Med & 22 & 54004 & 180 & Native Hawaiian & No & 3 \\
17 & M & High & 20 & 54000 & 180 & American Indian & L.O. Alzheimer’s & 3 \\
18 & M & Low & 21 & 54001 & 180 & Black & Parkinson’s & 3 \\
19 & F & High & 24 & 54003 & 180 & Asian & Hereditary Thromophilia & 3 \\
20 & M & No & 25 & 54000 & 122 & American Indian & Celiac & 3 \\
21 & M & No & 26 & 54001 & 166 & Asian & L.O. Alzheimer’s & 3 \\
22 & F & High & 23 & 54003 & 180 & Asian & Parkinson’s & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Users’ Weight Loss Profiles.  
Figure 1: Overview of the Proposed Multi-faced Anonymity Analytical Approach.