Bibliographic record
Abstract
Individuals are increasingly confronted with requests to identify themselves when accessing services provided by government organizations, companies, and other service providers. At the same time, traditional transaction mechanisms are increasingly being replaced by electronic mechanisms that underneath their hood automatically capture and record globally unique identifiers. Taken together, these interrelated trends are currently eroding the privacy and security of individuals in a manner unimaginable just a few decades ago. Privacy activists are facing an increasingly hopeless battle against new privacy-invasive identification initiatives: the cost of computerized identification systems is rapidly going down, their accuracy and efficiency is improving all the time, much of the required data communication infrastructure is now in place, forgery of non-electronic user credentials is getting easier all the time, and data sharing imperatives have gone up dramatically. This paper argues that the privacy vs. identification debate should be moved into less polarized territory. Contrary to popular misbelief, identification and privacy are not opposite interests that need to be balanced: the same technological advances that threaten to annihilate privacy can be exploited to save privacy in an electronic age. The aim of this paper is to clarify that premise on the basis of a careful analysis of the concept of user identification itself. Following an examination of user identifiers and its purposes, I classify identification technologies in a manner that enables their privacy and security implications to be clearly articulated and contrasted. I also include an overview of a modern privacy-preserving approach to user identification.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".