# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Anonymization and Pseudonymization of Data  
Anonymization and pseudonymization methods are critical in safeguarding confidential data [38]. These techniques replace directly identifiable information with non-identifiable alternatives, significantly reducing the risk of individuals being re-identified within the data.  
Anonymization involves removing or concealing all Personally Identifiable Information (PII) from a database. Techniques such as hashing, tokenization, and generalization replace specific data with non-identifying codes or modify it to remove specificity [39]. For example, in Table 3, the application of anonymization techniques overwrites the name of the subject with fictitious personal information, as illustrated in Table 4.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Student ID** & **Name** & **Data of Health** & **Consider** & **Aspliance** & **Equal** & **Please** & **Case** & **CPAs** & **Candidual** & **Candidual** & **Candidual** & **Candidual** \\ \hline
001 & J  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Student Name** & **Grades** & **Study Hours** \\ \hline [PERSON] & 10.0 & 55 \\ [PERSON] & 7.0 & 40 \\ [PERSON] & 9.0 & 42 \\ [PERSON] & 8.5 & 50 \\ [PERSON] & 6.0 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Student names before pseudonymization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subject Name** & **Disabilities** \\ \hline Rgwko [PERSON] & Vision impairment \\ [PERSON] & None \\ [PERSON] & Deaf or hard of hearing \\ [PERSON] & None \\ [PERSON] & Mental health \\ \hline \hline \end{tabular}
\end{table}
Table 4: Subject names after anonymization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Student Name** & **Grades** & **Study Hours** \\ \hline [PERSON] & 10.0 & 55 \\ [PERSON] & 7.0 & 40 \\ [PERSON] & 9.0 & 42 \\ [PERSON] & 8.5 & 50 \\ [PERSON] & 6.0 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Student names before pseudonymization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subject Name** & **Disabilities** \\ \hline [PERSON] & Vision impairment \\ [PERSON] & None \\ [PERSON] & Deaf or hard of hearing \\ [PERSON] & None \\ [PERSON] & Mental health \\ \hline \hline \end{tabular}
\end{table}
Table 3: Subject names before anonymization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subject Name** & **Disabilities** \\ \hline [PERSON] & Vision impairment \\ [PERSON] & None \\ [PERSON] & Deaf or hard of hearing \\ [PERSON] & None \\ [PERSON] & Mental health \\ \hline \hline \end{tabular}
\end{table}
Table 4: Subject names after anonymization.  
Data Noise (Perturbation): This technique, also known as data perturbation, consists of minor modifications to the dataset's attributes to make them less precise and remove possible identifications. It is crucial to understand the level of noise that should be applied to the data without compromising an individual's analysis or privacy.
* Generalization: This technique involves modifying attributes by changing their scale or order of magnitude to provide an overview of each attribute.
* Aggregation: This method condenses data into summarized versions with fewer attributes through standardization and grouping similar data. Aggregation differs from generalization in that it actively alters the data, as opposed to the basic setting applied to each attribute in generalization.  
The anonymization and pseudonymization techniques are essential for granting regulations such as GDPR and HIPAA. They enable organizations to lawfully process and share personal data for various purposes while mitigating the risk of unauthorized access and misuse.