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Pseudonymous data

Pseudonymous data is a type of data that has been processed in such a way that it can’t be traced back to an identified or identifiable natural person without using additional information. Pseudonymization can help keep personal data safe and prevent a possible data breach while enabling its use for purposes like research and data analysis.

Pseudonymization means an individual can still be identified through indirect or additional information. Unlike anonymized data, since pseudonymous data can be restored, GDPR considers it personal data.

Some common pseudonymization techniques include:

  • Scrambling – Mixing or obfuscation of letters.
  • Encryption – Encoding data to make it unintelligible and scrambled. In many cases, encrypted data is also paired with an encryption key.
  • Masking – Hiding the most important part of the data with random characters or other data.

Additional reading:

  • Privacy by design in practice: How “just enough” data beats “just in case” collection

    While collecting more data “just in case” feels safer, according to Matt Gershoff, it’s also one of the biggest sources of unnecessary compliance risk, analytical noise, and wasted organizational resources in the analytics industry today. His approach of “just enough” data collection is more intentional, more aligned with privacy regulation, and often more analytically effective.

  • 4 ways to make your analytics HIPAA-compliant: Implementation guide

    Healthcare organizations have four main approaches to achieving HIPAA-compliant analytics. Each has different trade-offs in cost, technical complexity, and analytics capabilities. This guide compares all four implementation methods – from using Google Analytics with workarounds to deploying fully HIPAA-compliant analytics platforms – so you can choose the right approach for your organization’s needs and resources.

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