Secure and Efficient LCMQ Entity Authentication Protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it