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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:

  • first party data

    First-party analytics without consent: Your Digital Omnibus compliance guide

    The Digital Omnibus is the European Commission’s simplification initiative to modernize the EU’s digital rulebook and reduce consent fatigue. The framework would enable first-party analytics without consent when specific criteria are met, ending years of uncertainty about the use of legitimate interest for web statistics.

  • University website personalization: First-party data strategies for student recruitment and retention

    University websites receive millions of visits annually from diverse audiences – prospective students, admitted students weighing their options, current undergraduates, graduate students, parents, alumni, and faculty. Yet most institutions serve identical content to all these visitors, missing critical opportunities to engage each audience with relevant information.

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