8 Data Anonymization Techniques to Safeguard User PII Data

In today's data-driven market, data translates to more power and opportunity for businesses. But as it is said, “With great power comes greater responsibility.” As more personal information is being collected and analyzed by organizations, the need to protect an individual's privacy and prevent the misuse or unauthorized access of the personal data comes with it. The Netflix Prize, a dataset released in 2006 to improve and innovate Netflix's recommendation algorithm, containing a large amount of user data from Netflix's movie recommendation service, including user ratings and rental histories, spurred the need for data anonymization. 

According to the DLA Piper’s latest annual General Data Protection Regulation (GDPR) Fines and Data Breach Survey, Europe have issued a total of EUR1.64bn (USD1.74bn/GBP1.43bn) in fines since 28 January 2022 under GDPR. A year-over-year increase in aggregate reported GDPR fines of 50%.

Let’s look at various data anonymization techniques available and tools offering those techniques.

Data Anonymization Techniques

Different data anonymization techniques can be used in many industries aiming to fetch useful insights from the data flowing while ensuring the data protection standards and regulations are met. 

1. Data Masking 

The data masking technique encrypts sensitive information within a dataset so that original data is protected while being used by businesses for analysis and testing. The sensitive data is modified in real time (As done in dynamic data masking) as it is accessed or by creating an alternative version of the database with anonymized information (as done in static data masking). This technique is commonly useful where data needs to be shared or accessed by different parties.

For example, personal identifying information (PII) such as social security numbers, names, and addresses can be masked by replacing them with randomly generated characters or numbers or by replacing not all but the last four digits with the ‘x’ of the social security number or credit card number.

Some common data masking techniques are as follows:

a. Randomization: This involves replacing the original data values with random or fictitious values that are generated based on a predefined set of rules. The random data is not linked to any identifiable information.

b. Substitution: This involves replacing the original data values with a masked value that retains the same data format and characteristics as the original value but does not reveal any identifiable information.

c. Perturbation: This involves adding a random noise or variation in a controlled manner to the masked dataset. This breaks the usual pattern of masking data, thus increasing the protection for sensitive information to be reverse-engineered.

2. Generalization

As the name suggests, this technique replaces specific data values with more general ones. The sensitive data is thus grouped into broader categories. For example, a person's exact age is anonymized with a more generic age range, such as 25-34. Thus, this type of technique can be applied to several types of data, such as demographic or transactional data. It is also important to balance the generalization being performed on the data so that it does not compromise the usefulness of data for analysis.

3. Data Swapping

In this technique, two or more sensitive data records are rearranged or swapped within a dataset. The anonymization is done by switching or exchanging values in a record with the corresponding values of another record, i.e., exchanging positions of two records within a dataset. For example, in a medical record containing sensitive information such as names or social security numbers, the swapping of values of certain fields will help protect patients' privacy while keeping all other records intact. The exchange in the values between two or more individuals within a dataset preserves the dataset's statistical properties and protects the individuals' identities.

4. Data Substitution

Data substitution involves replacing a piece of data in a dataset with a different piece of data. For example, if you have a dataset with values 1, 2, 3, and 4, and you substitute the value 2 with the value 5, the resulting dataset would be 1, 5, 3; Talend Data Fabric is a data integration and management platform including data anonymization capabilities which allows users to define and apply anonymization rules to their data. One of the techniques used in Talend's data anonymization is data substitution. With Talend's data substitution feature, users can define rules for substituting sensitive and unreal data values while preserving the data's overall structure and format.

5. Data Pseudonymization

This technique is considered less effective than other anonymization techniques, such as data masking, which ensures that anonymized datasets are difficult to retrieve. In this technique, the original PII is replaced with false identifiers or pseudonyms but maintains a specific identifier that can give access to the original data. Thus, the false identifier may or may not be directly linked to the individual's real identity. Data pseudonymization is often used where sensitive or personal data is not required for any business analysis or testing, but the identity of the individuals needs to be obscured. For example, in medical research, the identity of patients may need to be obscured as per the ethics and imposed legislation. However, some forms of patient identification may still be required to link medical records from different sources. It can be combined with hashing, encryption, or tokenization methods. For example, data such as names or identification numbers, into a fixed-length string of characters that is known as a hash or randomly generated token (a random alphanumeric code). It is a unique representation of the original data but cannot be reversed to identify or reveal the original data. The hash can then be used as a pseudonym for the original PII.

6. Data Permutation

Involves rearranging the order of the data in a dataset. For example, if you have a dataset with values 1, 2, 3, 4, and you permute the data, you might end up with a dataset that looks like 2, 1, 4, 3.

7. K-Anonymity

This is a data anonymization technique that helps protect an individual's private information in a dataset by ensuring that everyone is not identifiable from the others within the dataset. This is achieved by removing or generalizing unique identifiers data of everyone, such as name or Social Security number, etc. For instance, in a data set of 100 individuals, the value of K is 100, then hen no individual's information can be distinguished from at least 99 or K-1 others in the dataset.

K-Anonymity is a popular technique in data anonymization and is widely used in various fields, such as healthcare, finance, and marketing. K-Anonymity is considered an effective technique for protecting privacy, as it limits the ability of an attacker to identify specific individuals based on their attributes. One tool I have tried and recommended is K2View, which offers a K-Anonymity technique as part of its data anonymization capabilities through its patented micro-database technology. This involves grouping records with similar quasi-identifiers, such as age ranges or job titles, into a cluster. The records in each cluster share the same attributes for the quasi-identifiers, making identifying individuals based on these attributes difficult. Next, a unique identifier or value is assigned to the cluster that replaces the original quasi-identifiers. The sensitive data is mapped with the assigned unique identifier rather than the original quasi-identifiers, making it difficult to trace individual data subjects.

It is a technique designed to be flexible and scalable. Other variations of K-Anonymity, such as L-Diversity (including sensitive and general attributes) and T-Closeness, enhance privacy protection by considering the data diversity and distribution of sensitive attributes and general attributes, such as race or medical condition.

8. Differential Privacy

In this technique, a controlled amount of noise is added to the data before releasing it to protect the privacy of individuals. This controlled noise does not significantly affect the accuracy of the results of any analysis performed on the data; thus, it is a specific approach to perturbation-based anonymization. The amount of noise added to the data is determined by a parameter called the privacy budget.

Conclusion

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