Introduction to anonymization
Anonymization refers to the modification of information about individuals, which indicates the privacy of data and secures the data. The technique is an essential process to handle the data in all institutions and organizations. The main goal of anonymization is to ensure that the data is combined data sets and to protect the personal data. The method is particularly used in strict data protection laws such as the General Data Protection Regulation and health insurance portability in the United States with the required data anonymization and a comprehensive system to ensure safe practices and elimination of risk of exposure to an individual’s identity. Moreover, various techniques to anonymize the data, which involve the elimination of complex problems and making the data effective.
Importance of anonymization in privacy preserving
Anonymization is also known as the process of identifying where it is a major technique for data storage in a digital environment, such as cloud platforms and databases. The main aim of anonymization is the protection of data with the help of masking identifiable information and tracing the data. The method is mainly useful in large organizations for detailed information and efficient workflow management. With information architecture, which involves a secret layer in which the original data, such as user ID and IP addresses, will be present (MAJEED & LEE, 2021). The authorization layer that is only accessible to authorized users and analysis of grouped data. However, with the anonymization technique, there is complete protection and also the elimination of risk.
Anonymization operations for privacy in big data
Suppression is a technique that replaces it with a placeholder and makes the information specific to the entire data value. Generalization is also a method of a broader aspect towards various values and relies on a value generalization hierarchy that abstracts various categories. The K-anonymity helps in following micro data and identifies the attributes. The main idea is to use quasi-identifiers to make both generalization and suppression into records and reduce the risk of reidentification.
Techniques of anonymization
Data masking
Data masking involves transformation of data into complex version that cannot be accessible to unauthorized access. The technique involves original database and offshore replacement of any many characteristics. Moreover, the technique is most difficult to manage the data or guess the data it eliminates the risk of exposure of data.
Pseudonymization
Pseudonymization is a method which is replaced with various identifiers. Some of the names get changed with other names this approach is statistically analyzed and also eliminate the risk of privacy. Also, supports various environments in the research of software testing.
Generalization
Generalization is a method used for certain elements to protect identities. Instead of depending on the exact details, the data is converted into various values. For instance, state structures are simplified to just a street name or a city. The idea is to eliminate the prediction of data sets and also maintain privacy.
Data swapping
The data swapping is rearrangement of data with various words. The swapping helps in protecting the information from actual source. For example, switching birth dates between the individuals is difficult to identify. The method ensures redistribution of overall sets and also maintains connection.
Data perturbation
The method involves various levels of randomness in the dataset (Ishtake, Nirmal, & Lanjewar, 2024). The modifications are helpful in minimizing distortion and also protecting privacy without compromising the data and maintaining the masking of various information.
Techniques of anonymization

Anonymization algorithms
Data fly algorithm
The data fly algorithm is a method that uses single-dimensional full domain generalization. It identifies the frequency of quasi-identifier values and generalizes most of the unique values. The data fly ensures meeting data sets and also producing a generalized version of information. Moreover, contributes to the entire system and combination. One of the major strengths of Data Fly is adjusting the data to the privacy level.
Mondrian algorithm
Mondrian algorithm uses multidimensional generalization for the identification of data sets and uses a quasi-identifier to refine the process. In addition, selects the normalized range and chooses the informed values, and sums up the frequencies.
Anonymization algorithms

Anonymization methods
Anonymization refers to the modification of data that no longer reveals the personal information of a person. It is also known as de identification and the technical definition of sharing private data. Moreover, involves major techniques such as K anonymity, L diversity, and T closeness.
K-anonymity
Generally, the k-anonymity refers to the individual record of quasi-identifiers (Gosain & Chugh, 2014). The method achieves this through various methods such as suppression and generalization to make the data identifiable and also susceptible to severe privacy techniques and multiple releases of data.
L-Diversity
L-Diversity is built upon K-anonymity, which ensures a group of records and sensitive attributes are identified with the help of quasi-identifiers. The method prevents the attackers from learning sensitive information and also records to a particular (Gajjar & Divecha, 2020). However, L-Diversity has limitations of distinct values, and also data is added through analysis.
T-closeness
T-closeness requires the distribution of sensitive attributes to all people and to satisfy the data set. As the data grows in the company, anonymization becomes challenging in a large-scale dataset so which is a concern for a large volume of data. The models are especially useful for data usage and permissions in the context of multiple predictions, and also for managing user privacy.
Use Cases of Data Anonymization
Healthcare and Medical
Both medical professionals and researchers analyze the health trends in specific groups, which depend upon anonymized data to protect the patient information. The method ensures privacy and protection of medical details.
Marketing and customer engagement
The e-commerce platforms and digital marketers make use of anonymized data to understand the behavior and send relevant updates to the customers. The method experiences various websites and social media platforms to use anonymization technique.
Developing and testing software
Generally, software teams require real world data to identify the bug and also improve the performance in software system. There are less development environments with less secure practices anonymization helps using the personal information and enhancing the security.
Workplace analytics and efficiency
The company gathers much data on employees to enhance its operations and create a safe working environment. The anonymization helps in attaining actionable insights and developing a culture of compromised individual privacy.
Conclusion
Many advancements in data, which continue to evolve, privacy preservation techniques. In summary, the major organizations and service providers that share massive datasets and also make decisions. Also, possesses various risks to reidentification and protects the data. Various anonymization techniques, such as data masking, generalization, data swapping, and more, which offer a high level of privacy protection for data sets. The techniques such as K-anonymity, L diversity, and T-closeness business which provide evaluation and implementation of anonymization techniques. The various challenges in the data context that improve cross-references and also external identities.
References
Gajjar, H., & Divecha, N. (2020). DIFFERENT TECHNIQUES FOR PRIVACY PRESERVING IN BIG DATA: COMPARATIVE. International Journal of Creative Research Thoughts (IJCRT), 08(05), 3791-3795. Retrieved from https://ijcrt.org/papers/IJCRT2005503.pdf
Gosain, A., & Chugh, N. (2014). Privacy Preservation in Big Data. International Journal of Computer Applications, 100(17), 44-47. Retrieved from https://research.ijcaonline.org/volume100/number17/pxc3898322.pdf
Ishtake, S., Nirmal, S., & Lanjewar, G. (2024). Privacy Preserving Big Data Publication on Cloud Using Anonymization Techniques with Deep Neural Networks. Journal of Systems Engineering and Electronics, 34(06), 394-402. Retrieved from https://jseepublisher.com/wp-content/uploads/42-JSEE2342.pdf
MAJEED, A., & LEE, S. (2021). Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey. IEEE, 09, 8512-8545. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9298747
Keywords
Data Anonymization, Privacy protection, Data masking, Big data, Anonymization algorithms
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