An Efficient and Lightweight Deniably Authenticated Encryption Scheme for e-Mail Security
Bibliographic record
Abstract
The most important security requirements to secure electronic mail (e-mail) systems are: confidentiality, authentication, non-repudiation and data integrity. In conventional e-mail systems, Secure/Multipurpose Internet Mail Extensions (S/MIME) and Pretty Good Privacy (PGP) digital envelops are used to satisfy these security requirements. However, confidentiality and authentication are performed in two different phases, which increases computations and leads to more energy consumption. Moreover, the receiver can easily reveal the source of the message, violating the sender's privacy. In this paper, we propose a low-cost deniably authenticated encryption scheme (DA-ENS), where all the cryptographic primitives are being performed in a single logical step to achieve these goals. Experimental results show that our scheme, DA-ENS, achieves low computational cost and communication overhead at 80-bit, 112-bit, 128-bit, 192-bit and 256-bit security levels. Energy consumption is shown to be reduced to 80%, 67%, 42%, 62% and 48% compared to similar schemes SL+BF, LXJ+BF, Fagen Li et al. (FL), AJL and CZJZJSZ respectively. Also, we have proven that, our scheme DA-ENS is provably secure in random oracle model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".