Comparing the benefits of pseudonymisation and anonymisation under the GDPR
Bibliographic record
Abstract
Many organisations are trying to obtain more value from their data to improve their products and services, offer new ones and optimise their own internal operations. For example, more chief data officers, or similar roles, are being created to drive such data-enabled transitions. With the General Data Protection Regulation (GDPR) in place, these organisations need to determine the lawful basis for such activities. De-identification techniques, such as pseudonymisation and anonymisation, can play an important role in facilitating such secondary uses and disclosures of data. In regard to de-identification, the GDPR introduces nuances that have not previously been seen, recognising the existence of different levels of de-identification and explicitly adding references to pseudonymisation as an intermediate form of de-identification. This paper explores the nuances introduced by the GDPR, compares the benefits of the different levels of de-identification found in the regulation, and provides practical guidance for using de-identification as a tool for addressing different GDPR compliance obligations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.209 | 0.404 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".