A pragmatic approach to privacy risk optimization: privacy by design for business practices
Bibliographic record
Abstract
This paper introduces Nymity’s Privacy Risk Optimization Process (PROP), a process that enables the implementation of privacy into operational policies and procedures, which embodies in Privacy by Design for business practices. The PROP is based on the International Organization for Standardization (ISO) concept that risk can be positive and negative; and further defines Risk Optimization as a process whereby organizations strive to maximize positive risks and mitigate negative ones. The PROP uses these concepts to implement privacy into operational policies and procedures. This paper was produced by Nymity and the Office of the Information and Privacy Commissioner of Ontario, Canada. It was presented by Terry McQuay, President of Nymity, at “Privacy by Design: The Definitive Workshop,” in Madrid, Spain, on November 2nd, 2009. The workshop was hosted by Dr. Ann Cavoukian, Information and Privacy Commissioner of Ontario, Canada, and Yoram Hacohen, Head of the Israeli Law, Information and Technology Authority.
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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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".