Data Anonymization Technologies

In today's data-driven market, data brings more power and opportunities to enterprises. But as the saying goes, 'Great power comes with great responsibility.' With the increasing collection and analysis of personal information by organizations, the need to protect personal privacy and prevent misuse or unauthorized access to personal data also arises.

According to the latest 'General Data Protection Regulation (GDPR) Fines and Data Breach Investigations' report by Euwa Law Firm, since January 28, 2022, Europe has imposed fines totaling 1.64 billion euros (approximately 1.74 billion US dollars / 1.43 billion pounds) under the GDPR. The total amount of GDPR fines reported increased by 50% year-on-year.

To better protect user personal privacy data, we need to understand various available data anonymization technologies and the tools that provide these technologies.

Data Anonymization Technologies

Different data anonymization technologies can be used in various industries, aiming to gain useful insights from data streams while ensuring compliance with data protection standards and regulatory requirements.

1. Data Masking

Data anonymization, also known as data whitening, data de-privacy, or data transformation, refers to encrypting sensitive information in a dataset to protect the original data when used for analysis and testing in enterprises. In cases involving user security data or some commercially sensitive data, real data can be modified and provided for testing under the condition that system rules are not violated, such as personal information such as ID numbers, phone numbers, and card numbers, which all require data anonymization.

This technique is usually very useful when data needs to be shared or accessed by different parties. For example, personal identification information (PII) such as social security numbers, names, and addresses can be replaced with randomly generated characters or numbers, or all digits except the last four can be replaced with 'X' in social security numbers or credit card numbers, thereby protecting data security.

The following are some common data anonymization techniques:

a. Randomization: This includes replacing the original data value with random or fictional values generated based on a predefined rule set. Random data does not link to any identifiable information.

b. Replacement: This involves replacing the original data value with a masking value that retains the same data format and features as the original value but does not display any identifiable information.

c. Perturbation: This includes adding random noise or changes to the masked dataset in a controlled manner. This breaks the conventional data anonymization patterns, thereby enhancing the protection of sensitive information.

2. Generalization

As the name implies, this technology replaces specific data values with more general data values. Sensitive data can be modified to a series of ranges or a large area with reasonable boundaries, or some identifiers can be deleted to maintain the accuracy of the data. For example, a person's exact age can be anonymized to show a more general/wide age range, such as 25-34 years old. Therefore, this technology can be applied to various types of data, such as demographic data or transaction data. It is worth noting that balancing the generalization of data execution is also very important so that it does not harm the usefulness of the data for analysis.

3. Data Swapping

This technique refers to rearranging or swapping two or more sensitive data records within the dataset. Anonymization is achieved by swapping or exchanging the values of one record with the corresponding values of another record, i.e., swapping the positions of two records in the dataset. For example, swapping the values of certain fields in medical records that contain sensitive information such as names or social security numbers can help protect the privacy of patients while maintaining the integrity of all other records. Swapping values between two or more individuals in the dataset not only preserves the statistical properties of the dataset but also protects the identity security of individuals.

4. Data Substitution

Data substitution involves replacing data blocks within the dataset with different data blocks. For example, if you have a dataset with values 1, 2, 3, and 4, and you replace the value 2 with the value 5, the resulting dataset will be 1, 5, 3. For example, the data anonymization feature in the data integration and management platform Talend Data Fabric allows users to define and apply anonymization rules to their data. One of the techniques used in Talend data anonymization is data substitution. Users can use Talend's data substitution feature to define rules for replacing sensitive and fictitious data values while retaining the overall structure and format of the data.

5. Data Pseudonymization

This technique is considered less effective than other anonymization techniques (such as data masking), which ensure that the anonymized dataset is difficult to retrieve. In this technique, the original PII is replaced with pseudonyms or aliases, but retains specific identifiers that can access the original data. Therefore, pseudonyms may be directly or indirectly related to an individual's real identity. Data pseudonymization is usually used in any business analysis or testing that does not require sensitive or personal data, but needs to conceal personal identities. For example, in medical research, patient identities may need to be blurred according to ethics and mandatory legislation. However, some form of patient identity verification may still be required to link different sources of medical records.

It can be used in conjunction with methods such as hashing, encryption, or tokenization. For example, converting data such as names or ID numbers into fixed-length strings called hashes or randomly generated tokens (random alphanumeric codes). This is a unique representation of the original data and cannot reverse identify or display the original data. Then, this hash can be used as a pseudonym for the original PII (Personal Identifiable Information).

6. Data Permutation

This method involves rearranging the order of data within the dataset. For example, if you have a dataset with values 1, 2, 3, 4 and you sort the data, the resulting dataset may look like 2, 1, 4, 3.

7. K-Anonymity

Anonymity is achieved through techniques such as generalization (more generalized and abstract descriptions of the data) and suppression (not publishing certain data items), releasing data with lower precision, so that no one in the dataset can be identified from others, thereby helping to protect personal privacy information in the dataset. This is achieved by deleting or generalizing the unique identifier data of each individual, such as name or social security number. For example, in a dataset of 100 individuals, if the value of K is 100, then no individual's information can be distinguished from at least 99 or K-1 other individuals in the dataset.

K-anonymity is a popular data anonymization technique that is widely used in various fields such as healthcare, finance, and marketing. K-anonymity is considered an effective technique for protecting privacy because it limits the ability of attackers to identify specific individuals based on their attributes. The recommended tool for this technology is K2View, which provides K-anonymity technology as part of its data anonymization functionality through its patented microdatabase technology. This involves grouping records with similar quasi-identifiers (such as age range or position) into a cluster. The records in each cluster share the same attributes of the quasi-identifiers, making it difficult to identify individuals based on these attributes. Next, a unique identifier or value is assigned to the cluster to replace the original quasi-identifier. Sensitive data is mapped to the assigned unique identifier instead of the original quasi-identifier, making it more difficult to track individual data subjects.

It is a flexible and scalable technology. Variants of K-anonymity, such as L-Diversity (including sensitive attributes and general attributes) and T-Closeness, enhance privacy protection by considering the diversity and distribution of data for sensitive attributes and general attributes (such as race or medical condition).

8. Differential Privacy (Differential Privacy)

Differential privacy (abbreviated as DP) is a cryptographic technique used to protect privacy. By adding noise to the results of queries, the actual results of the query operation are hidden or blurred until they cannot be distinguished, thereby protecting sensitive data. This controlled noise does not significantly affect the accuracy of any analysis results performed on the data; therefore, it is a specific method of anonymization based on perturbation. The amount of noise added to the data is determined by a parameter called the privacy budget.

Conclusion

Organizations have now recognized that the scalability and cost-effectiveness of cloud computing can meet their data anonymization needs. As this data anonymization is a trend, it is expected to continue in the coming years as more and more organizations recognize the benefits of cloud-based solutions for their data management needs. It is very important for organizations to invest in effective data anonymization solutions to ensure the security and privacy of their data.

Original link:

https://dzone.com/articles/8-data-anonymization-techniques-to-safeguard-user