Privacy by design: the definitive workshop. A foreword by Ann Cavoukian, Ph.D
Bibliographic record
Abstract
Positive-sumIn November, 2009, a prominent group of privacy professionals, business leaders, information technology specialists, and academics gathered in Madrid to discuss how the next set of threats to privacy could best be addressed.The event, Privacy by Design: The Definitive Workshop, was co-hosted by my office and that of the Israeli Law, Information and Technology Authority.It marked the latest step in a journey that I began in the 1990's, when I first focused on enlisting the support of technologies that could enhance privacy.Back then, privacy protection relied primarily upon legislation and regulatory frameworks-in an effort to offer remedies for data breaches, after they had occurred.As information technology became increasingly interconnected and the volume of personal information collected began to explode, it became clear that a new way of thinking about privacy was needed.Privacy-Enhancing Technologies (PETs) paved the way for that new direction, highlighting how the universal principles of fair information practices could be reflected in information and communication technologies to achieve strong privacy protection.While the idea seemed radical at the time, 1 it has been very gratifying over the past 15 years to see it come into widespread usage as part of the vocabulary of both privacy and information technology professionals.But the privacy landscape continues to evolve.So, like the technologies that shape and reshape the world in which we live, the privacy conversation must
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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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".