# Analytics on Anonymity for Privacy Retention in Smart Health Data +
## 6 Conclusions  
Smart health has significant impacts on healthcare and wellness. However, it also poses privacy threats to users. As health data get larger and become more accessible to multiple parties, users lose more control of their data that increasingly become vulnerable to attacks. Furthermore, the challenge is not only to protect the data but also to ensure that the shared data are sufficiently informative. Increasing users' anonymity is a basic remedy as anonymity increases indistinguishability. The more indistinguishable people are the more anonymous they become and thus, their information and identity are better concealed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Avg. Info. Loss**Query Attribute** & **Age** & **Zip** & **Smoker** & **Sex** & **Chol.** & **BS** \\ \hline Before Anonymization & 0.61 & 0.99 & 0.12 & 0.11 & 0.85 & 0.07 \\ \hline \textless{}[Age, Zip], 5> & 0 & 0.4 & 0.12 & 0.11 & 0.85 & 0.07 \\ \hline \textless{}[Age, Zip, Smoker, Sex], 5> & 0 & 0.4 & 0 & 0.11 & 0.85 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Post anonymization analytics of Cleveland Dataset 1.  
This paper presents an approach to health data analytics focusing on anonymity for privacy protection. The approach is applicable to both data producers (e.g., use of fitness trackers, or glucose and heart rate monitors) as well as data consumers (e.g., weight loss application services, healthcare professionals) to safeguard a given health data set from information leakages and re-identification. A common concept relies on making data anonymous.  
An analytical approach is proposed to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) transforming the data into a more anonymous form by generalization using attribute hierarchies, and (3) anonymization that balances anonymity requirements and optimal informativeness by an automated Artificial Intelligence search using two simple heuristics. Unlike existing techniques, our anonymization approach preserves maximum information by avoiding extensive generalizations yet still complies with the anonymity requirements. The proposed anonymization follows \(k\)-anonymity; therefore, it inherits the limitations of \(k\)-anonymization as discussed in [21]. We describe and illustrate the detailed approach and analytics including pre and post anonymization analytics. We have conducted experiments to evaluate effectiveness of our anonymization approach. The results obtained show that our approach balances the trade-off between preserving privacy and retaining maximum information with efficient computational cost. Future work includes a framework designed to integrate all different measures to improve anonymization techniques as well as to better increase anonymity and protect privacy. The added metrics will help further the analysis of the anonymized data in terms of privacy. That way, we aim to get a better understanding of what needs to be improved for anonymization or how successful the anonymization is.  
Conceptualization, R.H.; Investigation, S.A.; Methodology, Both; Supervision, R.H.; Validation, R.H.; Data Curation, S.A.; Writing-original draft preparation, S.A.; Writing-review & editing, R.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in [UCI Machine Learning Repository] at [[[http://archive.ics.uci.edu/ml/index.php](http://archive.ics.uci.edu/ml/index.php)]([http://archive.ics.uci.edu/ml/index.php](http://archive.ics.uci.edu/ml/index.php))], reference number [31].  
The authors declare no conflict of interest.