A Stamped Hidden-signature Scheme Utilizing The Elliptic Curve Discrete Logarithm Problem
Bibliographic record
Abstract
Based on the anonymity that digital signatures provide to users and messages, digital signatures can be classified as hidden, weak, interactive, or strong blind signatures. The hidden blind signature hides the signed message from the signer’s vision during his interaction with an honest requester. Later on, after revealing the message the signer can easily link the message-signature pair. The hidden blind signature application deals with message anonymity only and cannot be done through a strong blind signature; the notary service is one example of a hidden blind signature. In this paper we propose a hidden blind signature scheme that utilizes bilinear pairing over elliptic curves. The proposed scheme requires smaller key sizes for the same level of security compared to schemes not utilizing bilinear pairings. The proposed scheme allows the signer to add information in the signed message. The requester cannot modify either this information or the signed message. This added information stamps the sig- nature with a certain date and place which we see as an essential requirement in applications such as notary ser- vice (testament application) and patent time proof. In notary service, there is no conflict of interest between the signer and the requester of the signature. There is no need to have a trusted party to authenticate the temporal or spatial information. Instead, the signature requester will embed this information into the message body which is hidden from the signer. After issuing the signature by the signer, the requester can verify that the signature has the designated date and place. This is under the assumption that the signer has to perform the signing process on the same day and that she is free to sign at any time that day. This date-stamping is very important in case the signers’ signature key is stolen or compromised. The proposed scheme is proved to be secure against an existential adaptive chosen message attack.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".