Secure and Efficient LCMQ Entity Authentication Protocol
Bibliographic record
Abstract
The simple, computationally efficient HB-like entity authentication protocols based on the learning parity with noise (LPN) problem have attracted a great deal of attention in the past few years due to the broad application prospect in low-cost RFID tags. However, all previous protocols are vulnerable to a man-in-the-middle attack discovered by Ouafi, Overbeck, and Vaudenay. In this paper, we propose a lightweight authentication protocol named LCMQ and prove it secure in a general man-in-the-middle model. The technical core in our proposal is a special type of circulant matrix, for which we prove the linear independence of matrix vectors, present efficient algorithms on matrix operations, and describe a secure encryption against ciphertext-only attack. By combining all of those with LPN and related to the multivariate quadratic problem, the LCMQ protocol not only is provably secure against all probabilistic polynomial-time adversaries, but also transcends HB-like protocols in terms of tag's computation overhead, storage expense, and communication cost.
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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 | 0.003 | 0.005 |
| 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 | 0.005 |
| Research integrity | 0.002 | 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".