Scalable, Server-Passive, User-Anonymous Timed Release Cryptography
Bibliographic record
Abstract
We consider the problem of sending messages into the future, commonly known as timed release cryptography. Existing schemes for this task either solve the relative time problem with uncontrollable, coarse-grained release time (time-lock puzzle approach) or do not provide anonymity to senders and/or receivers and are not scalable (server-based approach). Using a bilinear pairing on any Gap Diffie-Hellman group, we solve this problem by giving scalable, server-passive and user-anonymous timed release public-key encryption schemes allowing precise absolute release time specifications. Unlike the existing server-based schemes, the trusted time server in our scheme is completely passive - no interaction between it and the sender or receiver is needed; it is even not aware of the existence of a user, thus assuring the privacy of a message and the anonymity of both its sender and receiver. Besides, our scheme also has a number of desirable properties including a single form of update for all users, self-authenticated time-bound key updates, and key insulation, making it a scalable and appealing solution. It could also be easily generalized to a more general policy lock mechanism
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".