Data anonymization: Pros, Cons & Techniques in 2024

Updated on Jan 2

4 min read

Table of contents

What is data anonymization?Why is data anonymization important?6 techniques of data anonymization Advantages of data anonymization Disadvantages of data anonymization Further readings:

Most retailers are investing in personalized marketing to deliver the right message to their customers. One of the best examples of a personalized product recommendation engine is Amazon Personalize which the company generates 35% of its revenue.

In order to provide customers with individualized experiences, most companies collect personal information about their users while they are shopping and browsing online. However, there are numerous cyber threats (Figure 1) that businesses should be mindful of to prevent data leakages.

Figure 1: Cyber threat landscape

Data anonymization is one of the strategies that defend the sensitive, personal, and confidential data of individuals against cyberattacks. Thus, we deeply introduce data anonymization, the methods that ensure it, and its benefits in this article to enable your company to acquire personal data with the fewest possible leaks.

What is data anonymization?

Data anonymization is the process of preserving sensitive and private information by removing or encrypting personally identifiable information (PII) (any information that can be used by third parties on its own, such as an address, email, ID number, etc.) from a database. Anonymized data prevents the identification of a specific person by maintaining the anonymity of the data source.

Why is data anonymization important?

If you run a business that collects and processes consumers’ personal data, you have to comply with privacy regulations such as GDPR in the EU and CCPA in California to protect the identities of your users. As long as the data of identifiers is cleaned, companies can share anonymized data externally for commercial purposes without putting the privacy of users at risk.

6 techniques of data anonymization

1. Data masking

Data masking is one of the Privacy Enhancing Technologies (PETs). It replaces all of the original sensitive data by hiding it with different data masking techniques ( see Figure 1) including:

Shuffling
Encryption
Character scrambling, etc.

Figure 1: Data masking process

Here is an simple example of data masking:

Name: Nathe, InsuranceNo: 669330287

The identification of sensitive data is the first step in the data masking process. For instance, in the example below, insurance no may be labeled as sensitive data by the organization. Then, the insurance no is masked and replaced with another authentic-looking data, like:

Name: Nathe , InsuranceNo: XXX-XXX-XXX

Feel free to check out more on data masking including its techniques, use cases, and best practices.

2. Generalization

In the generalizing data method, certain values of the attributes are replaced with a broader category to make it less identifiable. For example, the value ‘9500’ of the attribute ‘Wage’ may be expressed as a range, such as a wage ≤ 10000 or 5000 < wage ≤ 10000

3. Data swapping

The original data is substituted with random data. It swaps dataset attributes and shuffles the data elements randomly. It is hard to identify users because of the mismatch.

4. Data perturbation

Data perturbation preserves personally identifiable information by adding random noise to a data set. The actual database’s content can only be accessed by authorized users.

5. Pseudonymization

Pseudonymization is a data management and de-identification method that substitutes actual sensitive data with fictional data. In the pseudonymization technique, personally identifiable information such as names is replaced with an artificial identifier, or pseudonym ( is a fictitious name ). For instance, organizations might substitute “Axl Rose” with a fictitious name like “Martin Smith” in order to maintain anonymity in their actual record.

6. Synthetic data

Synthetic data is information that is produced artificially rather than organically. It is generated to protect the privacy and confidentiality of actual data. It is an irreversible solution to protect sensitive data, making it impossible for an authorized person to identify the sensitive information of your users.

To learn more about synthetic data, check out our article on the topic: What is Synthetic Data? What are its Use Cases & Benefits?

Advantages of data anonymization

Prevent data misuse

Privacy regulations such as GDPR and CCPA have strict regulations to provide strong protection of personal data. Techniques for data anonymization enable businesses to adhere to regulations and protect them from data misuse.

Prevent being a data breach victim

A data breach occurs when an unauthorized third party accesses confidential data or shares it without proper authorization. It results in physical or material damage, putting an individual’s freedom in danger, for example.

Laws governing data breaches vary from country to country, but under the GDPR in Europe, businesses are required to:

become aware of data branches,
anonymize data for privacy,
obtain user consent before processing data
report a data breach to ICO.

Disadvantages of data anonymization

Reduce the quality of insights from a database

Deriving value from customer insights is crucial for companies to create a more personalized experience for their customers and refine their marketing strategies. Therefore, businesses collect a lot of customer information through a variety of methods, including asking clients directly and monitoring their online behavior. However, collecting this data without the consumer’s consent is a violation of the law. Data privacy regulations such as GDPR and CCPA are altering how companies capture, store and share consumer data with third parties to provide strong protection of personal data.

That’s why data anonymization is a method that many companies use. However, anonymous data restricts the ability to turn data into knowledge. Since personal information is eliminated through the data anonymization process.