Data Scrambling vs Data Masking: Impact on Data Integrity and Privacy

Data scrambling and data masking offer two distinct approaches to protect sensitive data, with different impacts in terms of integrity and privacy. 

At a time when the average cost of a data breach has surpassed 4 million dollars, and with increasing scrutiny from both regulatory bodies and consumers, comprehensive data privacy strategies have become imperative. 

In this quest, a number of techniques like data scrambling have emerged as tools for protecting sensitive data. However, these techniques are fundamentally different in their purposes and approaches. As a result, the choice of data protection method has distinct implications in the accuracy and confidentiality of the resulting dataset.

In this article, we look at what exactly is scrambled data, what are the main differences between data scrambling vs data masking, and some key aspects on how to pick the right approach to achieve data privacy and compliance.

Data scrambling vs. data masking: the definitions

Definition and purpose of data scrambling

Data scrambling is a technique for protecting sensitive information that relies on obscuring it by altering its values. 

While there are many techniques and approaches to obtain scrambled data, some fundamental data scrambling techniques rely on altering data by means of shuffling or rearranging data values.

As such, data scrambling prevents access to original data values by transforming datasets and rendering them unintelligible. 

Examples of data scrambling techniques

• Shuffling: this technique involves rearranging the values of a single attribute within a column or, in some cases, across various ones. This means each value remains valid, their original association with other values in the dataset is disrupted, as a strategy for helping to protect sensitive information.
• Random substitution: it involves substituting characters in data values by different ones, without following a specific logic. For instance, customer names are replaced by random letters so that "Alice" is substituted by "Xrjeq". A possibility that offers more consistency is lookup substitution, which replaces original data values with alternatives from a predefined lookup table, making substitutions repeatable.

Definition and purpose of data masking

Data masking covers a series of techniques that aim at creating datasets where sensitive data is hidden while also maintaining the original data’s referential integrity and consistency.

In other words: in data masking, original sensitive data values are safeguarded by creating a different dataset; however, attention is paid to maintaining the original data’s representativeness, coherence, and functional richness. This makes data masking a valid technique for protecting sensitive data while also ensuring it remains useful in non-production environments, such as software development or testing.

Data masking techniques are designed to be an irreversible process, meaning that, when looking at masked data, sensitive information is protected and the original data values are not accessible. 

You might be interested: Why cyber security needs data masking

Examples of data masking techniques

• Nulling out: a technique based on removing or blanking out sensitive data fields.
• Redaction: removes sensitive information from a dataset so that it is no longer accessible. The fields can be replaced completely or partially in different ways, an example being the use of masked generic patterns (such as “***” replacing the original values).
• Date or number variance: dates and numbers on a list can be altered by adding or subtracting a fixed amount (100 days, 5% increase…).

Impact of both techniques on data integrity and privacy

The distinctions mentioned above greatly define how data scrambling or other types of data masking are different in terms of the resulting dataset’s integrity and privacy. As such, understanding these differences is fundamental to judge when to apply each method, depending on the intended use case.

Effects on data quality and integrity

The different data masking techniques prioritize protecting sensitive data while also keeping it usable. The goal is to maintain data quality, integrity and consistency across fields, so that masked datasets can be employed in non-production environments.

These different techniques exist on a spectrum offering varying levels of data quality and integrity. In the case of simple data scrambling via shuffling, it prioritizes speed and format preservation, but may compromise referential integrity. As such, the more basic data scrambling techniques, on their own, are not the best choice for functional testing.

The different use cases and industry applications

Data masking applications

Data masking builds safe and privacy-compliant datasets that also retain the original data’s characteristics. As such, they’re useful for safeguarding sensitive data while also obtaining intelligence in the following contexts:

• Software development
• Functional testing
• Analytics
• Research
• Collaboration with third parties
• Training

Data scrambling applications

On its own, data scrambling can be a good strategy for protecting data that isn’t sensitive and doesn’t require data privacy compliance. It is also useful in scenarios where maintaining relationships isn’t a priority, in which scrambled data represents a quicker and simpler approach than other more complex types of masking.

Additionally, data scrambling can be employed to further enhance data protection when used alongside other masking techniques.

How to implement and considerations for choosing the best option based on needs

The choice of the best data masking technique largely depends on factors like the specific goals the data is used for, or the particular data that an organization is dealing with, including notions around its sensitivity. 

As such, some helpful aspects to consider for professionals looking at data scrambling vs. data masking include:

• What are the goals? Advanced data masking techniques are the most adequate for obtaining realistic testing results, or in training and development contexts. Meanwhile, if the main goal is simply to obfuscate data, data scrambling represents a fast and simple alternative.
• What is the level of data sensitivity? Advanced data masking allows for further control and is thus more adequate for protecting sensitive data and compliance.
• Is referential integrity important? Some data scrambling techniques don’t prioritise referential integrity, while other data masking techniques do, and are thus more appropriate if integrity is essential for the project.
• Is irreversibility important? Both scrambled data and masked data offer different degrees of irreversibly altering datasets. 

As mentioned above, a combination of both techniques (and others) may be key to match the project’s goals and ensure compliance.

Future of data protection: how businesses and IT professionals can adapt to emerging trends and technologies

Data protection has evolved to become a key strategic area for companies, a complex scenario that is likely to evolve in the near future following a number of trends: 

• A rise in digital threats which target sensitive data, as noted by specialist reports and surveys across the globe. For instance, the security firm Kaspersky reported a 26% surge in phishing attempts in 2024, and a 14% increase in the number of detected malicious files.

• Data inhabiting increasingly complex digital ecosystems. With the rise of cloud services and other hybrid and heterogeneous infrastructure, professionals face the need to protect data across very diverse and dynamic environments. This makes it difficult to obtain a unified view of where sensitive data resides, complicating its protection.

• Increasingly strict data privacy regulation and scrutiny, with regulations appearing across the globe that aim at protecting citizens’ privacy rights. Today and in the near future, data mishandling involves not only serious legal issues for businesses and the possibilities for penalties, but also the risk of reputational crises that erode consumer trust and can seriously undermine businesses. 

In this scenario, IT professionals and businesses are looking for the right tools and techniques that will allow them to strike a balance between compliant data protection and obtaining intelligence from data. 

As such, advanced software tools are emerging that offer comprehensive approaches to data masking and compliance. From data protection software to Test Data Management tools, these are platforms designed to facilitate data security across complex digital ecosystems.

For non-production environments, icaria TDM represents a solution for fast, secure data provision. Acting as a potent sensitive data masking tool, the platform goes one step beyond by ensuring data integrity and confidentiality in testing and development.

In order to do so, icaria TDM is designed to:

• Optimize sensitive data detection through its Sensitive Data Mapping capacities, which identify, classify and track location and flow of such data, providing a unified visibility.
• Align with data privacy regulations, making regulatory compliance simpler for businesses and guaranteeing test data is appropriately protected in non-production environments. 
• Automate processes in test data preparation to speed up results without compromising security. 
• Easily integrate with other tools.

The tool offers an extensive catalog of masking and pseudonymization algorithms. This allows businesses and IT professionals to secure sensitive data according to their goals and needs, safely leveraging data in non-production environments.

Orange’s success story using icaria TDM bears witness to this tool’s potential in not just applying data masking, but fully optimizing test data management practices. Faced with the need to improve their protocols for accessing high-quality data for its software testing, the French company managed to reduce test execution times by more than 70% and improve the quality of tests, while also reducing their data storage costs. The tool also opened the door for greater team autonomy in test data management.

Looking for the right data masking strategy? icaria TDM is a key ally for streamlining test data practices, guaranteeing compliance and security while taking speed and automation to the next level. 

Learn more about icaria Technology and get in touch with us to speak to our team about how we can help you.