Anonymous Credentials for Privacy-Preserving E-learning
Bibliographic record
Abstract
E-learning systems have made considerable progress within the last few years. Nonetheless, the issue of learner privacy has been practically ignored. Existing E-learning standards offer some provisions for privacy and the security of E-learning systems offers some privacy protection, but remains unsatisfactory on several levels. On the other hand, privacy preserving solutions that are appropriate and used in E-commerce environments are inadequate and unsuitable to the context of E-learning. Indeed, while in most E-commerce applications different transactions between the client and the system are pretty much independent, in E-learning the interactions between the learner and system are intertwined into a developing process that depends heavily on the path the leaner is following. In this paper, we introduce the Anonymous Credentials for E-learning Systems (ACES), a set of protocols to preserve learner’s privacy in E-learning environments. In particular, the ACES allows learners to provide anonymous credentials throughout the learning process, such as when they need to prove that they possess the necessary requirements to register for a course, and/or to prove that they are the legitimate owners of an Anonymous Transcript or an Anonymous Degree. Although the concept of anonymous credentials is not novel, ACES takes into account the specificities of E-learning. Moreover, in order to prevent the misuse of privacy, ACES prevents the possibility of sharing credentials between learners.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".