## 4 Enhancements to ShinyAnonymizer Hashing Techniques
### Implementation of Advanced Techniques such as Hashing
#### 4.1.4 XXHASH64  
* Description: Bottom coding is a way of separating data into larger groups, especially if factors have a broad range of results. Income levels, for instance, may be categorized as \"low\", \"medium\", or \"high\", without offering details. * Application: XXHASH64 is a fast, non-cryptographic hash function that determines a 64-bit hash. It was built for speedy processing and has been revised to immediately hash huge datasets. Figure 2 illustrates briefly how our hashing process works: Data are initially extracted from the database, and in addition to identifying the significance of the data and any privacy issues, a complete risk evaluation follows. This analysis helps in selecting the most suitable hashing method. For example, despite its recognized weaknesses, due to its speed, MD5 can be chosen for more significant data. However, SHA-256 or SHA-512 may be selected for more sensitive data due to their more considerable level of security. Salting is often employed, together with hashing, to offer a higher level of security, while minimizing the risk of hash collisions--where two different sources produce a single hash value. Thedata are hashed if the risk examination has ended and the most suitable hashing method has been established. To achieve this, every piece of data must be turned into a hash code, while maintaining its trustworthiness and usefulness for research [21]. To show the various levels of sensitivity demanded for security, patient genders in a dataset can be hashed utilizing MD5, while patient races can be hashed using SHA-512. After being transferred for utilization, the hashed data are thoroughly examined to verify their precision and accuracy, reducing security risks during transmission or storage. While hashing has several security and computing productivity benefits, it also has limitations. The key benefit is the fact that it can rapidly and accurately validate data, which is demanded for maintaining data integrity and secure procedures for authentication. The selection of hashing algorithm is important, however; while powerful algorithms, including SHA-512, provide greater protection at the cost of greater processing requests, quicker methods, including MD5, are simpler but more open to attacks. Also, when trying to ensure that each input generates a unique hash, methods including salting have to be utilized, owing to the issue of hash collisions. Organizations may properly enhance their data privacy practices and maintain private data security, while keeping the data's usability, by carefully selecting and utilizing the relevant hashing algorithms. Utilizing hashing as an advanced transformation method provides advantages as well as drawbacks. Its strength is in its computational effectiveness, which renders it suitable for the fast integrity of data audits and the authentication of passwords. However, adopting an appropriate hashing algorithm is essential, as smaller algorithms could expose problems. In addition, hash functions are vulnerable to hash collisions, in which multiple inputs produce an identical hash value. Salting, which introduces arbitrary data to every input before hashing, attempts to reduce this problem [22]. Table 3 shows an initial dataset, including sensitive patient data, in the context of healthcare. Together with data including gender, ethnicity, marital status, and preferred language, every record has an exclusive identification (PatientId). For example, the dataset includes FB2 ABB23-C9D0-4D09-8464-49B70B982F0F0, a male patient who is African-American, married, and speaks Icelandic. While these particular and identifiable data are required for internal use, exposing them could seriously compromise privacy. Figure 2: A visual representation of the hashing process. The business uses hashing techniques to enhance data security, producing the dataset in Table 4. Numerous cryptographic methods are used to hash sensitive features, including PatientGender, PatientRace, PatientMaritalStatus, and PatientLanguage.