A Privacy-Preserving 3rd-Party Proxy for Transactions that Use Digital Credentials
Bibliographic record
Abstract
In this paper we propose modifications and extensions to the digital credentials issuing and showing protocols to make them appropriate for an e-commerce environment in which the user has only a hand-held constrained device (such as a PDA or a cell phone), with limited memory and processing power. In particular, this device does not hold the digital credentials or conduct the corresponding protocols; this is done by a 3rd party (a proxy) on behalf of the user, who simply needs to authorize the transaction once it is complete. Our proposal frees the user from having to carry the digital credentials and protocol engine with him/her at all times (which may be unrealistic in some environments), while retaining the desired privacy properties (e.g., the 3rd party proxy performs computations on the user's behalf and participates in the required protocols without learning any of the user's private information). The complete architecture that we describe also includes mechanisms to prevent the following three forms of attack: password cracking, betrayal, and collusion.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".