Improved Strongly Deniable Authenticated Key Exchanges for Secure Messaging
Bibliographic record
Abstract
Abstract A deniable authenticated key exchange (DAKE) protocol establishes a secure channel without producing cryptographic evidence of communication. A DAKE offersstrong deniabilityif transcripts provide no evidence even if long-term key material is compromised (offline deniability) and no outsider can obtain evidence even when interactively colluding with an insider (online deniability). Unfortunately, existing strongly deniable DAKEs have not been adopted by secure messaging tools due to security and deployability weaknesses. In this work, we propose three new strongly deniable key exchange protocols—DAKEZ, ZDH, and XZDH—that are designed to be used in modern secure messaging applications while eliminating the weaknesses of previous approaches. DAKEZ offers strong deniability in synchronous network environments, while ZDH and XZDH can be used to construct asynchronous secure messaging systems with offline and partial online deniability. DAKEZ and XZDH provide forward secrecy against active adversaries, and all three protocols can provide forward secrecy against future quantum adversaries while remaining classically secure if attacks against quantum-resistant cryptosystems are found. We seek to reduce barriers to adoption by describing our protocols from a practitioner’s perspective, including complete algebraic specifications, cryptographic primitive recommendations, and prototype implementations. We evaluate concrete instantiations of our DAKEs and show that they are the most efficient strongly deniable schemes; with all of our classical security guarantees, our exchanges require only 1 ms of CPU time on a typical desktop computer and at most 464 bytes of data transmission. Our constructions are nearly as efficient as key exchanges with weaker deniability, such as the ones used by the popular OTR and Signal protocols.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".