Anonymization vs masking vs scrambling-discussing the semantics of data obfuscation.

Anonymization vs masking vs scrambling. Excuse meauw?

A look into the semantics of data obfuscation.

Snaplet automatically de-identifies your production data so that you can code against production-like data without worrying about PII. Snappy, our cat Mascot and CEO of naps and smiles recently commented on this statement: “Guys, we see you using all these fancy words, like anonymization, masking, de-identification, scrambling, obfuscating, redacting, masking, synthesizing… what do they actually mean?”

Each of these terms represents a different aspect of protecting sensitive information, and the choice of use depends on the specific use case and the level of protection required. So why do we sometimes use them interchangeably?

We realized that some of our readers might also be asking the same questions and therefore decided to explore each term, giving detailed definitions and examples.

Data transformation terms and examples

1. De-identifying data

Definition: De-identification involves removing or modifying personally identifiable information (PII) from a dataset, making it less likely to be linked to an individual.

Example: Changing specific names to generic labels, replacing exact ages with age ranges, or removing direct identifiers like social security numbers.

2. Data scrambling:

Definition: Data scrambling is a technique that involves randomly rearranging the characters or values in a dataset to protect sensitive information.

Example: Scrambling the order of credit card numbers in a database so that the original sequence is not discernible.

3. Redacting PII:

Definition: Redacting PII involves selectively editing or blacking out specific information to prevent its disclosure.

Example: Redacting names, addresses, or other identifying details in a document before sharing it publicly.

4. Data obfuscation:

Definition: Data obfuscation is a broader term that includes techniques like scrambling, masking, and anonymizing to make data more challenging to interpret.

Example: Applying various obfuscation methods, such as replacing characters, shuffling data, and adding noise, to protect sensitive information.

5. Anonymizing Data:

Definition: Anonymizing data involves removing or altering information in a way that prevents the identification of individuals in a dataset.

Example: Removing names, addresses, and any direct identifiers from a database to create an anonymous dataset.

6. Data Masking:

Definition: Data masking involves replacing, encrypting, or scrambling original data with a masked or pseudonymous version, ensuring that sensitive information is protected while preserving the format and structure of the data.

Example: Masking credit card numbers by replacing all but the last four digits with 'X' characters (e.g., 1234-XXXX-XXXX-5678).

7. Synthesized data:

Definition: Synthesized data is artificially generated data designed to resemble real data without containing actual sensitive information.

Example: Creating a synthetic dataset for development and testing purposes that mimics the properties of a real dataset without exposing real PII.

From the definitions above, it is clear that some of these terms are very similar in meaning and that is why we often use them interchangeably. Snaplet’s Snapshot feature offers the user different ways of anonymising their data. We introduced three transformation modes in 2023 to make the anonymization process quicker, while still giving the user full control of what happens to their data. Unsafe mode copies data for columns not specified in the config as is, without being transformed. Strict mode on the other hand, fails the capture if any columns, tables, or schemas not specified in the configuration are detected. Auto mode automatically transforms data for columns, tables, or schemas not specified in the configuration. Auto mode strikes a balance between Unsafe and Strict, executing specified transformations while automatically and safely transforming any unspecified columns. This gives you complete peace of mind that you're getting production-realistic data that's entirely safe to use (see Introducing transformation modes).

Snappy, Snaplet's cute cat Mascot, investiates the meaning of the term 'anonymization'

Data transformation for privacy protection

Data masking, data scrambling, obfuscating sensitive data, and redacting PII

All these terms involve altering or concealing data to protect sensitive information. They aim to preserve the structure and format of the data while reducing the risk of exposing personally identifiable information (PII).

De-identifying data, and anonymizing data

All share the goal of making data anonymous or at least reducing the likelihood of identifying individuals within a dataset. They often involve removing or modifying direct identifiers.

In summary, these terms share common goals related to data privacy and protection but may employ different techniques depending on the use case and the level of anonymization or obfuscation required. The choice between these methods often depends on the specific requirements of the task at hand, the nature of the sensitive information being handled and on the type of transformation mode used. Thus, it will depend upon the user which term fits their use case to the T. For that reason, we use them all. Because, we do it all!

Data generation for unit testing and local development

In addition to data transformation, Snaplet can also generate production-like data suited to your specific requirements. We call this synthesized data generated by Snaplet Seed. Is this the same thing as fake data or random data? Let’s look at the definitions of ‘synthesized data’, ‘fake data’, and ‘random data’ to see how it differs.

  • Synthesized data: Generated to mimic the statistical properties of real data, maintaining realistic patterns.
  • Fake data: Completely fabricated information used for testing without any attempt to mimic real-world statistics.
  • Random data: Information generated without a specific pattern, providing diversity but not necessarily mimicking real-world distributions.

Snaplet Seed automatically creates data based on your schema which means that your generated data will mimic the properties of a real database, with realistic patterns as determined by your schema. As you can see, it differs from fake data because although it also generates fabricated information, it does so based on your schema, and/or common ai-learned database structures. In other words it creates production-like data that resembles realistic scenarios. If you need data to code against and you don’t have access to production credentials, Seed is the way to go. It is the quickest way to try out Snaplet and to get acquainted with our data generation and transformation capabilities. Click here to get started!

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Almarie Stander
May 3, 2024