What is Data Anonymization? – Definition, Techniques & Use Cases

What is Data Anonymization? – Definition, Techniques & Use Cases


Businesses store data in online databases and environments more than ever before. In fact, 60% of the world’s corporate data is in the cloud. But do these businesses have the right tools to protect privacy-sensitive data? While there are many data privacy regulations that businesses must adhere to, such as GDPR in Europe, they do not always safeguard data from data breaches. 

According to Verizon, most of the breaches involve personally identifiable (PII) data and payment card data. Every time a business undergoes a data breach, it can become costly to take appropriate measures to minimize the damages and inform various stakeholders that the data concerns.

Next to that, it can have a negative impact on the reputation of the business, which can result in financial losses in the long run. This is why organizations should find preventive measures such as data anonymization to safeguard the sensitive data they store and process. 

How these preventive measures look like, which techniques can be used, and how to automate data anonymization with modern AI solutions, will be discussed in this blog. Let’s get started! 

What is Data Anonymization?

Data anonymization is a method of protecting confidential or personal information by deleting or altering personally identifiable data that are being stored in a dataset. The goal of data anonymization is to preserve the credibility of the data stored or exchanged and ensure compliance with strict data privacy regulations. 

Data anonymization process

The main criteria of anonymization according to the ISO standard (ISO 29100:2011) is that personally identifiable information (PII) is irreversibly altered so that the person can no longer be identified directly or indirectly. Therefore, financial information, contact details, health reports, and payment data that contain PII should be well-protected to adhere to strict data privacy regulations.

Now that you know what data anonymization is, let’s continue with how to anonymize data. 

How to Anonymize Data

To anonymize data, you would first need to identify PII in the dataset and then determine the right anonymization technique depending on the potential risk of breaches. There are various software solutions available that can support your use case and requirements, for example: 

Each of these software use a different set of data anonymization techniques, which we will discuss more in-depth in the following section. 

Data Anonymization Techniques

The following list consists of the most commonly used techniques to anonymize sensitive data:

  • Data Masking
  • Pseudonymization
  • Generalization
  • Data Swapping
  • Data Perturbation
  • Synthetic Data

Data Masking

Data masking is the process of making data accessible with modified values. Data masking can be done by modifying data in real-time (dynamic data masking) or by creating a mirror image of a database based on altered data (static data masking). Anonymization can be performed with a range of data masking techniques such as encryption, data redaction, character shuffling, value substitution, scrambling, etc.


Pseudonymization is a data de-identification method to replace private identifiers with pseudonyms (false identifiers). An example could be switching the name “Jane Smith” with “Janet Doe”. Pseudonymization ensures statistical precision while ensuring that data is confidential. This means that data can still be used for training, testing, and analysis purposes. 


Generalization is a technique to purposefully exclude some parts of data to make it less identifiable while retaining data accuracy. With this technique, data can be modified into a range of values with logical boundaries. For example, a specific address can be revealed without a house number, or the number is replaced within a range of 140 house numbers of the original one.

Data Swapping

Data swapping, also known as shuffling or permutation, is a technique that swaps and rearranges dataset attribute values making the data unmatched with the initial information. Switching attributes that include identifiable values such as social security number or date of birth, can significantly influence anonymization.

Data swapping is often used when dealing with identifiable data inside columns stored in excel files, for instance, customer or employee records.

Data Perturbation

Data perturbation is a technique that slightly modifies the initial dataset by adding random noise and using value rounding methods. The values must be proportional to the disturbance employed to retain data usability. For example, if the base used to modify the original values is too small, data cannot be sufficiently anonymized. And if the base is too large, the data may not be recognizable or usable. 

For example, often a base value of 5 is used for rounding values such as age.

Synthetic Data

Synthetic Data is algorithmically generated artificial datasets with no relation to any original case. This method is enabled by the use of mathematical models based on patterns residing in the original dataset. Such models include linear regressions, standard deviations, medians, or other statistical models useful for creating synthetic outcomes.

With the use of artificial datasets, there are no risks of compromising data protection and privacy as they don’t include real personally identifiable information.

Some of these techniques may have crossed your path at some point if your organization works with privacy-sensitive data. If not, we hope to enlighten you in the next paragraph on whether it is relevant for you by presenting various data anonymization use cases. 

Data Anonymization Use Cases

To keep this blog readable, we only cover the data anonymization use cases that we come across most often. The following list is not exhaustive:

  • Remote Client Onboarding
  • Financial Information Processing
  • Software & Product Development

Remote Client Onboarding

Organizations that need to verify and store their client information during the remote onboarding processes are subject to various regulations such as KYC, GDPR, and AML to name a few. Often times clients need to scan their identity documents in order for the business to verify their identity or perform customer due diligence.

To protect PII such as social security numbers (SSN) or date of birth, from being misused, organizations may apply data anonymization through various masking techniques.     

Financial Information Processing

Financial institutions need to protect the privacy of their customers when processing financial information. Often that can be done by removing or obscuring PII from data sets using data anonymization techniques such as data masking or generalization. 

These techniques can be applied to various types of financial information, such as transaction reports, credit reports, payment information, invoices, bank statements, and loan applications. 


Software & Product Development

Developers need to use real data when developing software and tools to overcome real-life problems, perform testing, and improve existing solutions. The reason why the data is often anonymized is that the development environment can be vulnerable to breaches due to leakages or data being shared across multiple teams. This can ultimately lead to sensitive data becoming compromised.

Why You Should Anonymize Data

There are various reasons why you should anonymize data. The most important reasons may include the following :

  • Safeguarding against data misusage: Data anonymization ensures that internal stakeholders cannot misuse the data as well as minimizes the risk of data being exploited if the organization ever gets breached by external perpetrators.  
  • Data sharing opportunities: Data that has personally identifiable information cannot be shared with third-party companies, which creates a limitation in finding new business opportunities. However, with data anonymization, companies can share data with partners or researchers to gain new insights and develop new products. For instance, anonymized data can be used to train machine learning models to improve products and services.  

While it is important and can be beneficial for your organization to anonymize data, there are some disadvantages that you may want to consider.

Disadvantages of Data Anonymization

Some of the disadvantages of data anonymization follow:

  • Loss of the data utility: Regulations demand websites to receive permission from visitors to gather personal information such as cookies and IP addresses. However, removing identifiers and anonymizing data may restrict the ability to make use of the data in results. For example, anonymized user data cannot be used for personalized marketing or targeting purposes. 
  • Relies on technical resources: Anonymizing data can be a technically and resource-intensive process. Organizations would need to have specialized knowledge and expertise to implement. Next to that, it can be time-consuming and costly to maintain. Due to sophisticated hackers and data breach methods, companies must continuously update their anonymization techniques to ensure that data remain truly anonymous. 

Now that you have an idea of the pros and cons of data anonymization, we will explain how you can anonymize data. 

Anonymizing Your Data with Klippa DocHorizon

If you want to anonymize data from documents that you collect, digitize, and extract, Klippa can help you. Our Intelligent Document Processing software DocHorizon uses Optical Character Recognition (OCR) to extract text from images and AI models to recognize, classify and anonymize data according to your needs. How?

DocHorizon can be trained to blackline and mask certain fields and text from documents that are sent to the parsing engine. These documents can be sent via mail, web, or mobile application in the form of JPG, PNG, and PDF for example. After the data anonymization is applied, you can receive the anonymized data in the form of your choice including JSON, XLSX, XML, or CSV.

Implementation of our data anonymization solution is very easy due to the proper documentation available and can be done via API or SDK. Our API will be useful for you if you want to build your own information extraction and anonymization pipeline and connect it with your existing software systems. 

Our SDK, on the other hand, enables you to turn your mobile devices into data capture devices with the ability to mask data selectively. This is useful if you want to add data anonymization features to your existing or soon-to-be-released mobile app.

With DocHorizon, you can reap the following benefits: 

  • Retained data utility while automatically extracting and anonymizing data
  • Enhanced compliance with data privacy regulations and requirements
  • Reduced costs as you don’t need to buy multiple solutions to create your data anonymization pipeline
  • Faster turnaround times of data anonymization and processing with automation
  • Enabled scalability with low dependency on human resources

Ready to automate your data extraction and anonymization? Simply fill in the form below to get a free demonstration of our software. If you have any further questions, contact our experts for more information.

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