The trouble with Article 25 (and how to fix it): the future of data protection by design and default
Bibliographic record
Abstract
... What requirements does the new European data protection law impose on regulated entities regarding the use of privacy technologies across all aspects of product development? When the European Union adopted the Data Protection Directive in 1995 it included a recital instructing data controllers to ‘implement appropriate technical and organizational measures’ for safeguarding personal data ‘both at the time of the design of the processing system and at the time of the processing itself’.1 Over the next quarter-century, this idea of designing in privacy from the outset took hold in both Europe and the USA. What then Ontario Privacy Commissioner Ann Cavoukian famously called ‘privacy by design’ (or ‘PbD’)2 progressed from a non-binding recital in Directive 95/46, to a recommendation of the European Commission (EC),3 the European Data Protection Supervisor (EDPS)4 and then the 32nd International Conference of Data Protection and Privacy Commissioners,5 to a proposed article in the General Data Protection Regulation (GDPR).6 The final text of the Regulation christened Article 25 as a new general obligation of controllers (and processors) to implement ‘data protection by design and default’.7 But what does this mean? In particular, does it require controllers and processors8 to embrace privacy engineering in full and adopt ‘state of the art’ privacy technologies and advanced cryptographic techniqes for protecting user data?9
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 | 0.041 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.073 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.029 | 0.049 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".