Four applications where anonymizing can play a role

When it comes to protecting privacy-sensitive data through anonymizing, we often think of situations where this data is ultimately used for research or analysis purposes. However, there are more applications where anonymizing can play a crucial role. We’ve outlined the four most common applications for you!

Software Testing

Organizations heavily reliant on their software solutions often dedicate a significant amount of time to testing the respective software. This testing typically focuses on the functional use of the software, with the goal of ensuring the organization is well-prepared for the software’s implementation (or a new version).

Testing is frequently conducted in a separate testing environment, often using a copy of the data of the production environment, allowing for testing as close to “production-like” as possible. This production-like testing is crucial. For example, you wouldn’t want something to go wrong during the implementation of new software because there was a difference between the production and test environments, leading to oversight in the testing process.

In such a situation, the organization wants the data to be as production-like as possible while ensuring the protection of privacy-sensitive data. Since testing software, in most cases, does not fall under the purpose-specific use of personal data, it’s important to secure personal data by anonymizing it. When applying anonymization, consideration is given to the testability of personal data in a way that does not negatively impact test results.

For example, the Social Security Number (SSN) is a national identification number that can easily link information from different files. Careless use of the SSN poses privacy risks, such as misuse of personal data and identity fraud. Suppose you anonymize an SSN by completely removing it, but the software automatically performs an SSN validation when accessing customer data. In that case, the software may generate an error because the SSN has been removed. This error could lead to an incorrect test result.

In this example, you might want to replace the existing SSN with a test SSN instead. In this case, the validation will function correctly, ensuring the test is successful.

Training and Educating Employees

In the case of specialized applications, organizations often want to train new employees in the use of the specific application before allowing them to work on production data. Given that the improper use of such applications can have significant consequences, this is certainly not a bad idea. But how do you ensure that new employees can be trained in a realistic manner?

Similar to the testing application, a separate environment is often set up specifically for training and practice. Activities performed by employees in a training environment have no impact on production, allowing for risk-free practice. However, the principle of purpose-specific use comes into play here as well, as training employees in the use of an application is often not the reason why you initially collected personal data. Additionally, employees are often not allowed to access all data, potentially limiting training opportunities.

One possible way to protect privacy-sensitive data in the training environment is by replacing it with specific training data. You can use predefined data that you place in the environment and base the training and exercises on this data. While this is a good approach to ensure standardized training, it often requires a considerable amount of time to set up and manage effectively. For instance, you must generate practice data for all functionalities covered during the training, and you often don’t want the same data to be used by multiple individuals simultaneously to prevent issues during training. Another crucial consideration is ensuring that all privacy-sensitive data within a training environment is securely removed. Especially in scenarios with extensive practice, new employees could accidentally encounter personal data while gaining hands-on experience with the software.

Anonymizing is also well-suited for this application, as it involves anonymizing existing personal data in the training environment. This allows new employees to explore and practice using the application freely without the risk of leaving personal data behind. Additionally, anonymization is generally less time-consuming than generating a specific dataset for each training session.

Software Development

A further extension of software testing is, of course, software development. During the software development process, many of the same considerations come into play as in software testing. The software developer will want to test and validate newly developed functionalities throughout the development process to ensure they function as intended.

What is often specifically observed in software development is the use of internally generated data tailored for a specific purpose. For example, a developer responsible for creating a customer may need data to test how the entered values can be validated before being processed in the application. In many cases, a developer generates their own dataset with fictional data for such purposes.

However, software developers also prefer to develop in an as much as possible production-like environment to prevent errors when the software is used by a customer. In many situations, internally generated data falls short because it may not have the unique characteristics that may occur during the actual use of the software. A developer will often use data of which they are certain it can (or cannot) be processed. In the “real world,” however, users can often be exceptionally creative in processing data, often in ways not initially foreseen by the developer.

By using anonymized data, you can maintain those unique characteristics (as long as they do not lead to direct identification, of course) so that they remain usable during development, allowing developers to incorporate them at an early stage.

Research and Analysis

Organizations are collecting more data than ever and increasingly using it for research and analysis purposes, such as gathering and combining data in a data warehouse.

In many cases, personal data plays an increasingly significant role in data analysis. Organizations, for example, may want to use personal data to improve the quality of their services or products, or to gain insights that become visible only when multiple data sources are combined.

As these analyses become more critical for organizations and their dependence on them grows, there is a growing demand to protect the personal data used for such purposes. Anonymization is, of course, an excellent solution, but its impact can play a significant role in analysis and research capabilities. In many cases, you want to maintain specific relationships. For example, suppose you are researching the number of residents in a postal code area. Anonymizing this data could potentially impact the number of individuals with a specific postal code. This could lead to incorrect conclusions from a research perspective. Therefore, in this application, it is crucial to have a clear understanding of what you want to investigate and analyze and tailor anonymization accordingly. There are anonymization methods that can keep the number of individuals in a postal code area constant while fully anonymizing the personal data on which it is based.

An additional challenge increasingly seen in research is the sharing of data between different organizations. From a research perspective, this provides a more comprehensive picture for conducting studies. However, it is essential that anonymized data provided by various contributing parties can be linked and anonymized uniformly. Pseudonymizing linking fields is one way to achieve this, but it requires extra attention to ensure that it is securely configured so that the pseudonyms themselves cannot be used to identify a person.