# Data Privacy and Ethical Considerations in Database Management
## 5 Results and Analysis
### Anonymization and Pseudonymization of Data  
To anonymize and pseudonymize the data in the dataset, we chose to pseudonymize the information in the \"Name\" column and anonymize the information in the \"Age\", \"DateOfAdmission\", and \"DischargeDate\" columns. In the column referring to the patient's name, we used the character replacement (masking) method to conceal the patient's last name, using unique values like \"**\" to avoid the direct identification of individuals. This method can be applied to either categorical or numerical attributes. In the column referring to the age of the patients, we applied a generalization method that groups the ages into 10-year intervals/ranges. This anonymization practice reduces the granularity of the data, making it less likely to identify specific individuals. Following this, we adopted a similar generalization approach for patient admission and discharge dates, where only the month and year were kept while the day was removed. By pseudonymizing the data in the column referring to the patient's name, we obtained the results in Tables 9 and 10: \begin{table}
\begin{tabular}{l l l} \hline
**Name** & **Gender** & **Test Results** \\ \hline [PERSON] & Female & Inconclusive \\ [PERSON] & Male & Normal \\ [PERSON] & Male & Normal \\ [PERSON] & Male & Abnormal \\ \hline \end{tabular}
\end{table} 
Table 7: Data before minimization. \begin{table}
\begin{tabular}{l l l} \hline
**Name** & **Gender** & **Test Results** \\ \hline [PERSON] & F & I \\ [PERSON] & M & N \\ [PERSON] & M & N \\ [PERSON] & M & A \\ \hline \end{tabular}
\end{table} 
Table 8: Data after minimization. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Age** \\ \hline [PERSON] & 81 \\ [PERSON] & 35 \\ [PERSON] & 61 \\ [PERSON] & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Age before generalization. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Gender** \\ \hline [PERSON] & F \\ [PERSON] & M \\ [PERSON] & M \\ [PERSON] & M \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Name after pseudonymization. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Age** \\ \hline [PERSON] & 80–89 \\ [PERSON] & 30–39 \\ [PERSON] & 60–69 \\ [PERSON] & 40–49 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Age after generalization \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Date of Admission** & **Discharge Date** \\ \hline [PERSON] & 17 November 2022 & 1 December 2022 \\ [PERSON] & 1 June 2023 & 15 June 2023 \\ [PERSON] & 9 January 2019 & 8 February 2019 \\ [PERSON] & 2 May 2020 & 3 May 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Dates before generalization. The results obtained with the implementation of anonymization and pseudonymization of the data in the dataset made it possible to remove and modify information that could identify patients, guaranteeing the privacy and security of the data. Data anonymization and pseudonymization provided additional protection, making it more difficult to identify individuals from the available data. By suppressing sensitive attributes and generalizing information such as age and dates, it was possible to preserve patient privacy without compromising data integrity for statistical and trend analysis. However, these anonymization and pseudonymization techniques have caused a reduction in data granularity, making detailed analysis or cross-referencing difficult. Ethical Considerations in Anonymization and Pseudonymization  
While these techniques enhance privacy, they also raise ethical concerns:  
* Re-identification Risk: Despite our efforts, there is always a risk of re-identification, especially with advances in data analytics. This presents an ongoing ethical challenge [18]. * Data Utility vs. Privacy: The reduction in data granularity may limit certain types of analysis, potentially impacting the utility of the data for research or healthcare improvements [72].