'Privacy by Design': Nice-to-Have or a Necessary Principle of Data Protection Law?
Bibliographic record
Abstract
Privacy by Design is a term that was coined in 1997 by the Canadian privacy expert and Commissioner for Ontario, Dr Ann Cavoukin, but one that has recently been receiving more attention in terms of its inclusion as a positive requirement into EU, US and Canadian data protection frameworks. This paper argues that the right to personal privacy is a fundamental right that deserves utmost protection by society and law. Taking privacy into consideration at the design stage of a system may today be an implicit requirement of Canadian federal and EU legislation, but any such mention is not sufficiently concrete to protect privacy rights with respect to contemporary technology. Effective privacy legislation ought to include an explicit privacy-by-design requirement, including mandating specific technological requirements for those technologies that have the most privacy-intrusive potential. This paper discusses three such applications and how privacy considerations were applied at the design stages. The recent proposal to amend the EU data protection framework includes an explicit privacy-by- design requirement and presents a viable benchmark that Canadian lawmakers would be well-advised to take into consideration.
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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 | 0.088 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.106 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".