Threshold Cryptosystem Based Fair Off-Line E-cash
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
The paper analyzed the security threats and system flaws of present e-cash schemes. Combining (t,n) threshold cryptography and e-cash, we present a threshold fair off-line e-cash scheme based on ECC ( Elliptic Curve Cryptosystem) . The scheme can trace the user identity and e-cash by embedding identity mark in e-cash generating and exchanging, and thus effectively prevents such illegal usage of e-cash as bribery and blackmailing, etc. By utilizing secret key sharing and probabilistic encryption algorithm, the scheme achieves threshold management of private key, avoids the misuse of identity tracing and currency tracing in fair e-cash scheme. The scheme achieves effective supervision on identity and e-cash tracing for fair electronic commerce, it also prevents coalition attack, intruder-in-middle attack and generalized e-cash forgery. Further analyses and comparison with other e-cash schemes also justify the scheme's brevity, security, high efficiency, and thus considerable improvement on system efficiency regarding software and hardware application.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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