Bibliographic record
Abstract
Abstract. HMAC is the most widely-deployed cryptographic-hash-function-based message authentication code. First, we describe a security issue that arises because of inconsistencies in the standards and the published literature regarding keylength. We prove a separation result between two versions of HMAC, which we denote and , the former being the real-world version standardized by Bellare et al. in 1997 and the latter being the version described in Bellare's proof of security in his Crypto 2006 paper. Second, we describe how (the FIPS version standardized by NIST), while provably secure (in the single-user setting), succumbs to a practical attack in the multi-user setting. Third, we describe a fundamental defect from a practice-oriented standpoint in Bellare's 2006 security result for HMAC, and show that because of this defect his proof gives a security guarantee that is of little value in practice. We give a new proof of NMAC security that gives a stronger result for NMAC and HMAC and we discuss why even this stronger result by itself fails to give convincing assurance of HMAC security.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".