Bibliographic record
Abstract
In the wake of recent revelations of mass government surveillance, secure messaging protocols have come under renewed scrutiny. A widespread weakness of existing solutions is the lack of strong deniability properties that allow users to plausibly deny sending messages or participating in conversations if the security of their communications is later compromised. Deniable authenticated key exchanges (DAKEs), the cryptographic protocols responsible for providing deniability in secure messaging applications, cannot currently provide all desirable properties simultaneously. We introduce two new DAKEs with provable security and deniability properties in the Generalized Universal Composability framework. Our primary contribution is the introduction of Spawn, the first non-interactive DAKE that offers forward secrecy and achieves deniability against both offline and online judges; Spawn can be used to improve the deniability properties of the popular TextSecure secure messaging application. We also introduce an interactive dual-receiver cryptosystem that can improve the performance of the only existing interactive DAKE with competitive security properties. To encourage adoption, we implement and evaluate the performance of our schemes while relying solely on standard-model assumptions.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".