Data anonymization vs data masking: is there a difference?

When it comes to data anonymization vs data masking, there is a significant amount of confusion and misinformation regarding the differences between these two terms, even within our own space. This confusion extends to similar terms such as data obfuscation and de-identification. 

In this article, we’ll provide clear definitions to level set on what each term means, and in particular, how you should think about them in the context of sourcing realistic test data for developers to use in software development and testing.

What is data anonymization?

Data anonymization is a fundamental process in the realm of data privacy. It involves altering personally identifiable information (PII) in such a way that the individual to whom the data belongs cannot be identified directly or indirectly. Anonymization is vital for protecting privacy and complying with international data protection regulations such as the GDPR, HIPAA, CCPA, and more.

What is the difference between data anonymization vs data masking (and other terms)?

Data anonymization encompasses a variety of techniques and approaches. Notably, it is synonymous with the term data de-identification.

Both data anonymization and data de-identification are umbrella terms to refer to a collection of more specific techniques such as data masking or data redaction. In this sense comparing data anonymization vs data masking does not fully make sense, since data masking is a form of data anonymization.

To further complicate matters, data masking is also synonymous with the term data obfuscation. You may encounter content comparing data anonymization vs data obfuscation, but again this comparison does not make sense.

Instead, we will define the ways in which data masking is distinct within the realm of data anonymization techniques and the use cases for which it is best suited. 

To recap:

• Data anonymization is synonymous with data de-identification.
• Data masking is synonymous with data obfuscation.
• Data masking is a form of data anonymization or de-identification.
• Each of these terms refer to processes that serve the end goal of protecting individual privacy.

What is data masking?

Data masking is a specific approach within the broader category of data anonymization. It involves creating a structurally similar version of existing real-world data that obfuscates the original data values while retaining the data’s usability for purposes like software testing and development.

There are two high-level approaches to data masking: static masking and dynamic masking.

• Static masking is performed on data at rest and irreversible, creating read/write data that is ideal for developer environments.
• Dynamic masking is performed on-the-fly by way of a database proxy to create read-only data, making it more suited to customer support or BI reporting use cases.

Data masking techniques are essential for organizations that need access to realistic data that offers a high degree of fidelity to real-world data while safeguarding sensitive information.

Uses for data anonymization and masking

Since data masking is a form of data anonymization, there is significant overlap in their use cases. That said, anonymization encompasses a broader swath of techniques, meaning that it can have use cases for which masking isn’t the most suitable approach.

Both data anonymization and masking have their use cases in the world of data management, and in particular, test data management. They are crucial for businesses that aim to leverage real-world data while adhering to strict privacy standards.

Data anonymization use cases

• Research: From academic research to market research to government research, data anonymization techniques allow personal consumer and citizen data to be used safely, without revealing personal information. Approaches suited to this use case include data generalization and tokenization.
• Business analytics: Similar to the research use case above, relying on anonymized data for business analytics achieves compliance and preserves consumer trust.
• Long-term data storage: Privacy regulations limit the timeframes for which data can be stored. Organizations that wish to store data beyond these timeframes may anonymize that data to ensure compliance. Data tokenization is an effective method for this use case.
• Data sharing or transmission: In certain instances, sensitive data needs to be anonymized in order to share or transmit it between organizations. If the organization needs to be able to re-identify the data post-transmission, data encryption is a method that provides a key to decrypt the data and return it to its original state.

Data masking use cases

• Software testing and development: Masked data is essential for software testing and development as it equips developers with data that behaves just like production data, enabling them to catch more bugs in testing and release higher quality products faster.
• Demo and training environments: Sales demos and employee training or customer onboarding all benefit from leveraging realistic masked data in their workflows, as masked data offers more comprehensive functionality than random dummy data.
• Managing data access: When role-based access control (RBAC) is the goal, dynamic data masking can be a good solution, as it dynamically masks data in real-time based on predefined role-based permissions. Customer service inquiry workflows are a good example of this.

Techniques for data Anonymization and masking

Data anonymization encompasses a variety of approaches, each of which encompass more specific techniques.

To clearly delineate the various techniques used in data anonymization vs data masking, the following table defines the most common approaches:

Solutions for data anonymization and masking

The importance of data anonymization and masking cannot be overstated in today’s data-driven world. Businesses need robust solutions to harness the value of their data without compromising on security and compliance. Tonic.ai offers industry-leading technologies tailored to meet these needs:

• Tonic Structural: Provides comprehensive data masking, synthesis, and subsetting capabilities that ensure high-fidelity, compliant test data for developers.
• Tonic Textual: Built for the anonymization of free-text data via redaction and synthesis, aiding compliance in model training and AI development.

By integrating these solutions into your engineering workflows, you can accelerate development, advance AI initiatives, and ensure that your data is both useful and secure.

To explore how Tonic.ai can transform your data anonymization and masking strategies, connect with our team or sign up for a free trial of Structural or Textual today.