Confronting the Dragons Without and Within: Privacy's Final Frontier? A Report on 'Terra Incognita': The 29th International Conference of Data Protection and Privacy Commissioners
Bibliographic record
Abstract
Hundreds attended the 29th International Data Privacy Commissioners Conference in Montreal from September 25 to 28, 2007. Provocatively named “Terra Incognita”, the conference was thematically organized around early explorers’ dealings with “unknown lands”. Working from folklore that early explorers regularly marked drawings featuring uncharted territory with the phrase “here be dragons”,conference presentations were organized around six “dragons”: public safety, globalization, law meets technology, ubiquitous computing, the next generation and the body as data. Responses to these “dragons” were variously described as dragon slayers, dragon tamers and dragon befrienders. These included multi-sectoral and inter-jurisdictional collaboration, privacy seals, de-identification, audits, and privacy impact assessments (PIAs).Conference participants were reminded on a regular basis that we are only minutes from the midnight of the total surveillance society symbolized by the ACLU’s Surveillance Society Clock. Some members of the privacy community attributed the gravity of the current situation to the limited inroads that have been made in generating privacy-friendly policy and in capturing the hearts and minds of the public more generally. Reverberating throughout most sessions at the conference was the centrality of collaboration across sectors, stakeholder groups and territorial jurisdictions. Central projects identified for these collaborative initiatives included reconceptualizing the meaning of privacy, dismantling the privacy vs. security dichotomy, and addressing deficiencies in existing legal approaches, with a focus on the limited shelf-life of “consent”, “control” and “property” models currently in use.Part I highlights some of what we learned about the “dragons” at the conference, focusing on globalization (including the security agenda), technology (including data mining, RFID, location-based tracking, genetics and biobanking, ubiquitous computing, nanotechnology and standard setting), future generations and Internet crime. Part II approaches the proposed toolkits for “dragon slaying”, “befriending” or “taming” (including multi-sectoral, inter-jurisdictional collaboration, privacy seals, de- identification, audits and PIAs). Part III steps back from specific issues to focus on some of the broader themes recurring throughout the conference that raise important concerns for future thinking (including the meaning of privacy, the privacy vs. security dichotomy, and deficiencies in existing legal approaches).
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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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.014 | 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".