Technology – Privacy Data Masking Techniques

Data masking techniques are a way of creating an alternate version of data that cannot be easily identified or reverse engineered. This alternative version will have the same format across many databases and preserve usability. Masking techniques are generally applicable to non-production environments that do not need actual data. Data masking is a way to meet GDPR requirements and offers many organizations a competitive advantage. It also makes data useless for cyber-attackers while preserving its usability.

Data Pseudonymization

The European Commission, data protection authorities, and industry should support state-of-the-art data pseudonymization techniques. Specifically, the European Commission should support research and development efforts to advance existing and future solutions and extend their scope and reliability. To make these techniques more robust and practical, data producers and processors should provide sufficient and accurate data. Depending on the situation, different types of data, background information, and implementation details, data pseudonymization techniques may not be suited to all situations.

For example, fintech companies need to identify certain groups or clusters of customers. Marketing data analysts and data engineers may need to identify audience clusters without knowing their names, but still be able to identify patterns of behaviour. Pseudonymization and anonymization techniques are a common way to identify such clusters and avoid the identification of specific individuals. This type of data minimization may also be beneficial to the GDPR.

Data Anonymization

One of the most important considerations in data masking is how to protect sensitive data without compromising its usability. The information must look realistic and consistent for valid test cycles, but masking methods cannot remove personal details entirely. In fact, data masking techniques are most effective on data that is not part of production systems. While enterprise computing often requires data to be extracted from these systems, data masking techniques are increasingly used to protect sensitive data.

The most widely known method of data anonymization is data masking. This method involves altering the values within a data set so that the original information cannot be recreated. Instead, artificial data is substituted for the original information, which has no relationship to the real values. Data masking techniques can be classified into two categories: static and dynamic. Static masking involves copying the data set into a file, while dynamic masking allows data to be changed on the fly. Other less common techniques include real-time and deterministic masking.

Lookup substitution

There are several types of data masking techniques. Some are used to mask sensitive data, while others are used to disguise an entire dataset. For example, a company might use a Luhn algorithm to verify data in a DB structure. In this case, the masking algorithm might replace the real first and last name with fake ones. Lookup substitution is the most common data masking technique, and it can be applied to several different types of data fields.

While data masking techniques have a low degree of data integrity, they do not completely fail. However, if the source data contains more values than the dictionary file contains, the operation will fail. If the dictionary file contains insufficient unique values, it will use nonunique values instead. Another type of data masking technique is known as lookup substitution. This type of data masking techniques is also prone to failure in application logic validation.


Several different redaction techniques are available for databases. The first is on-the-fly data masking. This technique encrypts values on-the-fly as they are returned from a data source. This type of data masking is most common in environments where application integration is seamless and the data flow is extensive. Additionally, organizations that use data feeds on a daily basis may not be able to afford the expense of masking them.

A common misconception regarding data masking is that redaction removes all identifiable data from data. However, this is not the case. The truth is that data masking techniques can remove personally identifiable information while maintaining consistency. They can also preserve the integrity of data records by replacing all characters with a single character. As a result, redaction can only be effective for non-sensitive data. For this reason, businesses should always use substitution methods whenever possible.

Another issue with data masking is the fact that it reduces the integrity of the data. By obfuscating sensitive data, you are making it more difficult for an application to perform logic validation. By obscuring data, it becomes impossible to reverse engineer the data that you masked. That means you must ensure the masked values are consistent across the database. This process should be repeatable and maintain the integrity of your data.

Date Switching

Many companies are using data masking techniques to protect sensitive production data. This type of masking obfuscates the real date by setting it back one hundred days. Data masking techniques require a detailed understanding of the data and must preserve the original format. Here are some tips to make data masking easier to use. In general, you want to mask only data that is sensitive. However, you should know that there are some cases when masking techniques are not appropriate for your situation.

The numeric variance method is useful when using date-driven data or financial information. For example, you can apply the same number and date variance to payroll data to mask a meaningful range. This method is also useful when dealing with sensitive data, such as credit card numbers. Despite the fact that numeric variance masking isn’t very effective, it’s an excellent choice if your data is sensitive. It makes it harder for the reverse engineer to identify individual values, and it’s also more secure.


Averaging data masking techniques can make your sensitive data more secure and private. You can replace your bank account number with a “x,” so that only the last four digits are verified. This way, your personal information cannot be used by fraudulent actors to make purchases. You can even use encryption to protect sensitive data. These techniques are a popular choice for protecting customer payment information. Here are three common use cases for data masking.

Substitution: This data masking technique replaces each numerical value in a table column with an average value, while keeping the unique look of the data. Redaction: This is the most straightforward technique. It replaces sensitive data with a generic value. It is often used to mask credit card numbers and phone numbers. Another technique called nulling replaces sensitive data with NULL. However, nulling has many disadvantages. The process may not be consistent, leading to inconsistency in the data.


Data masking techniques shuffle data so that no personal information is displayed, but it is still vulnerable to reverse engineering. One method uses data variance, which shuffles the values of important financial or transactional data. In simple terms, this technique substitutes the original purchase price with the highest and lowest price paid, and masks payment details by using the difference between the last and initial sale dates. Manual data masking is also effective in emergencies, and is especially helpful in times of crisis.

One type of shuffled data masking uses a random search file that replaces a field with another value. This method preserves the unique look of the data, and is suitable for many types of data. For example, a random search file can cover a customer’s name and address to prevent a data breach during a product or service test. However, random search files can be difficult to install. The other method, shuffling, is equivalent to replacement, but uses a single data mask field.

Data masking tokenization

The use of data masking is becoming increasingly important for securing unstructured fields. In addition to preserving the format, this method can help companies protect sensitive information. However, it has some drawbacks. One of them is the risk of reidentification. This process is essentially irreversible; however, it can be reverted to the original data in certain situations. To improve data security, organizations are using data masking to exchange data with third parties.

While the process may seem straightforward on paper, it involves a lot of hard work. Data masking requires careful planning and careful implementation. The key is to consider the nature of the data to determine which masking technique is best for the particular application. It must detect gender, since random changes in name can change the gender distribution. Typically, the range of values is restricted. Thus, a dataset that contains a high number of unique data items should use a masking method that respects these rules.

Another type of data masking is shuffling. This process involves moving data within rows and columns to give it an appearance of validity but not actual value. The process may vary in security, but the aim is the same: to prevent the identity of an individual from being revealed. Using data masking techniques is an excellent way to protect the privacy of sensitive information. The goal is to ensure that the data you share with third parties is not compromised, and that no unauthorized user will be able to decode the sensitive information.

Data masking techniques