Pseudonymous data is defined as information from which personal identifiers have been removed, replaced, or transformed in a manner that prevents direct attribution to an individual without additional, separately stored information. This replaces direct identifiers (e.g., names, identification numbers) with artificial markers such as codes or pseudonyms. According to Article 4(5) of the General Data Protection Regulation (GDPR), pseudonymization is the processing of personal data so that it can no longer be attributed to a specific subject without the use of supplementary information, which must be kept separately and securely (Voigt & Von dem Bussche, 2017).
Results indicate:
- Pseudonymization reduces the risk of unauthorized identification and data misuse.
- Unlike anonymized data, which is irreversibly stripped of all identifiers, pseudonymous data retains the potential for re-identification through a controlled “key” or supplementary dataset.
- Effective pseudonymization methods include:
- Replacement of names with unique codes
- Masking or hashing of sensitive fields
- Use of tokenization for high-risk identifiers
Example: “Patient_12345” instead of “Jane Doe.”
Discussion highlights:
- Data protection compliance: Pseudonymization is recognized as a safeguard under GDPR and other privacy frameworks, enabling organizations to process personal information while remaining compliant with data minimization and security requirements (GDPR Recital 29).
- Re-identification risk management: While pseudonymous data enhances privacy, it is not immune to re-identification if the key or mapping file is accessed. Therefore, strict separation and security protocols for the supplemental information are essential (Narayanan & Shmatikov, 2008).
- Research and analytics: Pseudonymous datasets allow for meaningful statistical analysis or longitudinal studies without exposing identifiable details. For instance, medical research often relies on pseudonymized patient records to balance scientific utility and confidentiality (El Emam & Arbuckle, 2013).
- Limitations: The effectiveness of pseudonymization depends on the strength of the method and the extent to which indirect identifiers are present. Weak pseudonymization may still leave data vulnerable to linkage attacks or inference if combined with auxiliary datasets.
In summary, pseudonymous data serves as a middle ground between identifiable and anonymous data, offering enhanced privacy protection while preserving analytical value. Its application is highly dependent on secure key management and robust technical controls to mitigate re-identification risks.
