Personas: Beyond Identity Protection by Information Control A Report to the Privacy Commissioner of Canada
Bibliographic record
Abstract
As individuals interact on larger and larger scales, what makes up their identity also has to expand. In a village, only a single name is enough. In a country, other attributes such as a social insurance or taxpayer number become necessary. Across the world, a multinational hotel chain may want to identify the same individual whenever he or she stays at one of their hotels, but there is no single global identifier that makes this possible. Because identities are important in so many interactions, not all of them known to and supervised by individuals, there are considerable economic and privacy risks when identity information is misused. Today, the standard solution to this problem is to mandate legal or policy rules that restrict the flow and use of identity information. This solution is starting to fail for two reasons. First, and most importantly, new developments in data mining and data fusion allow identities to be constructed from data that has not previously been considered identifying. Systems often do not control this kind of data as tightly as traditionally identifying data. Second, increasingly information that was supposed to be controlled is released accidentally. Once this has been done, there is no way to call it back, but also no way (short of Witness Protection Programs) to create fresh identities for
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".