## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques
### Technical Details of Encryption Techniques
#### 5.1.4 Aes512  
Description: For AES-512, in this methodology, this approach uses a 512-bit input and key size that provide more strength against cryptanalysis; however, the drawback of this is an extended authorized area. Application: AES-512 can find an application in any domain that needs strong security and a fast speed but has only restricted chip areas available for the block cipher. This would, therefore, apply to entertainment applications and even in satellite network communications. Figure 3 illustrates briefly how our data encryption process works: Selecting the proper database and then setting up the connection to it is the initial step in the data encryption method. The initial step ensures that the proper data repository is utilized for any following operations. The needed cryptographic abilities, like the pgcrypto module in PostgreSQL, will be enabled once linked. After all, has been set up, the next crucial step is to identify the sensitive data stored in the database. In-depth research takes place to assess the degree of sensitivity of the data that needs to be secured and the possible effects of disclosure. After gathering sensitive data, a detailed risk evaluation is carried out to identify the possible risks and weaknesses linked to the data. This can also help select the most effective encryption technique. After the risk evaluation finishes, proper encryption methods are selected. Concerning its high level of security and productivity, the Advanced Encryption Standard (AES512) is a suitable selection. According to the requirements, other methods, including RSA, Blowfish, or Triple DES, can also be utilized. After the algorithms have been selected, they are utilized to transform sensitive data into ciphertext, making them unreadable to other people. A confirmation step is executed after the procedure of encryption, to verify that the data have been securely encrypted without losing their authenticity or utility. In the end, the encrypted data are ready for safe transfer or storage, guaranteeing their protection for every moment of its presence. Important patient data in the healthcare industry must be controlled properly to maintain privacy and comply with rules like GDPR and HIPAA. Consider a job at a hospital that keeps a large database with patient data, like PatientId, PatientGender, PatientRace, PatientMaritalStatus, and PatientLanguage. Clinical studies, medical analysis, and epi  
Figure 3: A visual representation of the encryption process. demiological research all require this dataset. However, before these facts can be used for additional causes, they need to be anonymized, owing to the risk of data breaches. This example shows how the hospital removes these data utilizing the enhanced ShinyAnonymizer tool, preserving patient privacy while retaining the data's analytical utility. Three patients' details were included in the original dataset (Table 5):  
* Patient 1 is a married man of unclear race who talks Icelandic and can be tracked only by his PatientId;
* Patient 2: An African-American man who lives separately and understands English can also be properly identified by his PatientId;
* Patient 3: An Asian woman who has a husband and is skilled in English. A scenario of integrating several encryption models is provided in Table 6. Due to the sensitivity of such data points, the hospital utilizes the enhanced ShinyAnonymizer to alter every field employing strong encryption techniques. PatientId, for instance, is encrypted with the Data Encryption Standard (DES), generating a secure, anonymous word. Gender information is encrypted with Extended Data Encryption Standard (XDES), racial data utilizing Blowfish encryption, and marital status and preferences for languages with Advanced Encryption Standard (AES512). The enhanced dataset maintains the necessary format and statistics for study while deleting identifiable data, ensuring that one of the patients is unable to be re-identified utilizing the anonymized dataset.