Bibliographic record
Abstract
While the majority of the research on anonymity is focused on individuals, there is an increasing number of scenarios that demand anonymity for service providers as well. For example, in many business to consumer (B2C) scenarios, service providers sell their surplus to individuals for lower prices through arbitrageurs. Those service providers must remain anonymous, to avoid discouraging customers from buying directly from the service provider, at the regular price. Some providers wish to be anonymous for various reasons, including but not limited to, escaping denial of service attacks and avoiding censorship. Enabling service providers to prove relations to other providers is also needed in business to business (B2B) scenarios. This paper shows that allowing service providers to act as individuals in privacy preserving systems does not fulfill the job. The paper describes a system that allows individuals and service providers to participate in secure and anonymous transactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.012 | 0.034 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".