Data obfuscation is a form of data masking where data is purposely scrambled to prevent unauthorized access to sensitive materials. Data obfuscation is also known as data scrambling.

Why obfuscate data? If organizations mask their data, and experience a data breach, the data will be rendered useless and the organization will not be compromised. In addition to mitigating the risks of data breaches, several data privacy laws have also made data masking an absolute need! A few of the common data privacy laws are the General Data Protection Regulation (“GDPR”) and the California Consumer Privacy Act (CCPA). These have been issued to protect an individual’s personal data. At their core, these laws are designed to provide people with more control over their data. Data masking takes sensitive, classified or personal data and either removes or hides it – to then replace it by equivalent random characters, or dummy data. This ensures that the data remains intact, but without the sensitive, identifying information that shouldn’t be used or seen by other parties. By hiding sensitive data, a company is not as exposed to the risk of data breaches. In other words, data masking has the potential to protect an individual’s data and privacy, which is the overarching purpose of the data privacy laws that have come into effect.

How to obfuscate data? Three of the most common techniques used to scramble data include: encryption, tokenization, and data masking. Encryption and tokenization are reversible in that the original value of the data can be derived from the obfuscated data. Data masking is irreversible if done correctly.

Use a repeatable technique: When people pick a method to conceal data, it’s crucial to check that it produces the same results again and again when masking the same source data. If it doesn’t, the technique is not reliable and may not function as intended when needed.

Challenges of data obfuscation: The biggest challenge is planning, as it can consume a lot of time and resources. Data management is an enterprise-wide effort, so data owners, from each department and recipients of your scrambled data should be involved in planning any data scrambling efforts. Implementation can also be a significant effort. Interpreting the regulatory requirements is a key challenge that companies are facing.  Companies manage several different systems that were not specifically designed with the intent of hiding sensitive data or masking data. Given the variety in the technology, it becomes a challenge to adopt a single solution that can provide for a consistent output across the systems. Additionally, the volume of data that needs to be processed, does not make the effort any easier.

In conclusion, there’s a growing need for proper data obfuscation – resulting from the increasing volumes and magnitudes of data breaches, along with data privacy laws.


Author: Kate Strachnyi, Instructor at iData Quality Academy

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Your Data Quality Primer: Everything you need to know

Welcome to your Data Quality Primer from the iData Quality Academy – in this useful guide you’ll find everything you need to know to improve your understanding of data quality, assorted into useful categories for you. You don’t have to read through it in order, you can jump right to the section you’re interested in!

Your Data Quality Primer: Everything you need to know

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