The General Data Protection Regulation came into effect recently. The deadline for GDPR was 25th May 2018 and many organizations started making changes to their websites and policies in order to become compliant with the GDPR. While the world was busy figuring out and finding out everything there is to know about GDPR, many came across the words data anonymization.

What is Data Anonymization?

Techopedia defines data anonymization as a technique that will not take away the original field layout of the data being anonymized, so the data will still look realistic in test data environments.To put it in simple words, it is a process of eliminating personally identifiable information from data. This is a useful method for releasing information in a manner that the privacy of individuals can be maintained.

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What Are the Types of Data Anonymization?

Data anonymization can be further classified into four types as mentioned below:


As the name suggests, in this method of data anonymization you would be completely eliminating fields that may be used for identifying an individual. This type is considered a strong form of data anonymization.


Encryption presents a few difficulties and challenges such as generation of a sufficiently strong decryption key. Since data anonymization is not meant to be reversible, the management of decryption key is also a problem. In spite of being a strong form of data anonymization it is difficult to reverse.


Redaction is a process that involves removing fields that could be used to identify an individual. Redaction is considered a strong form of data anonymization.

Data Masking

Data masking is considered a weak form of data anonymization and consists of data scrambling and character replacement. However  the benefit of data masking is that it maintains the structure of data.

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Tools to Anonymize Data

For rendering the data you are currently working on as anonymous, you can make use of any of the below mentioned tools.

ARX Data Anonymization Tool

The good news is that ARX is an open source software that can be used for anonymizing sensitive personal data. By transforming datasets into syntactic privacy models this software mitigates attacks leading to privacy breaches.

NLM Scrubber

This tool is not completely developed and is in its beta stage. However the developers of this software have released a non beta version of the NLM-Scrubber for de-identifying medical documents and more.

Camouflage’s CX-Mask

CX-Mask tool is useful for de-identifying sensitive data that includes names, addresses, credit cards, SSN, phone etc. and retains the realism and functionality of the original data set.


Anonymizer detects faces, vehicle number plates and other image information in various scales and orientations nd helps you blur these images. This tool is excellent if privacy is a major concern.

Pseudonymization vs Anonymization

One of the biggest difference between pseudonymization and anonymization is that it allows some form of data re-identification however anonymization data cannot be re-identified. GDPR on the other hand allows and encourages the use of both pseudonymization and anonymization methods.


Data anonymization definitely helps individuals protect their identity, helping you make your organization more GDPR compliant.Which tool or technique should you make use of for this method however totally depends on your organization’s requirements and preferences.

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