Anonymous Credentials for Privacy-Preserving E-learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it