Regulating the cloud: a comparative analysis of the current and proposed privacy frameworks in Canada and the European Union
Bibliographic record
Abstract
Cloud computing is a growing phenomenon and promises greater efficiency and reduced-cost computing. However, some of the basic technological and business-related features of the Cloud are at odds with personal data protection laws. Canada and the European Union share similar core values related to privacy/data protection, and both regions aim to increase their competitiveness regarding cloud computing. Having these two similarities in mind, this paper explores the current legal and stakeholder landscape in Canada and the European Union with respect to cloud computing, data protection and how adoption of the model can be advanced. The analysis shows that neither of the frameworks is entirely compatible with cloud computing in its current application. Canada’s legal landscape is slightly more hospitable, but is lacking direction from regulators, while the EU’s non-harmonized and restrictive framework presents a challenge for cloud proliferation. Relevant stakeholders have diverging views on how data protection in the Cloud should be approached and 2012 will be a year during which these views will likely be debated in detail, in particular in response to the draft proposal of the European Commission on a new data protection framework. This paper concludes with distilling four possible options in this regard.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".