Proceedings of the 21st Workshop on Privacy in the Electronic Society
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 21st Workshop on Privacy in the Electronic Society (WPES'22). This is the twenty-first edition of WPES, a workshop intended to attract submissions from academia, industry, and government presenting novel research on all theoretical and practical aspects of electronic privacy, experimental studies of fielded systems, as well as perspectives of other communities such as law and business. To facilitate attendance to a global audience in times of the ongoing public health challenges, the workshop will take place both in person and online. Two types of papers will be presented: full papers, which are no more than 12 pages in the ACM double-column format, excluding the bibliography and well-marked appendix, and short papers, which are up to 4 pages for results that are preliminary or that simply require few pages to describe. The call for papers attracted 59 submissions (43 as full papers and 16 as short papers) from Austria, Belgium, Canada, France, Germany, Israel, Netherlands, Sweden, Turkey, and United States. Authors of 28 full paper submissions would like their submissions to be considered for short papers as well. Those submissions were evaluated by a program committee consisting of 51 researchers whose backgrounds include a diverse array of topics related to privacy. Each paper was reviewed by at least 3 members of the program committee, and the average number of reviews for each paper is 3.75. Papers were evaluated based on their importance, novelty, and technical quality. After the rigorous review process, 12 submissions were accepted as full papers (acceptance rate: 20.3%) and additionally 8 submissions were accepted as short papers.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.096 | 0.033 |
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".