HB <sup>đ</sup> entity authentication for low-cost pervasive devices
Bibliographic record
Abstract
The HB-like entity authentication protocols for low-cost pervasive devices have attracted a great deal of attention because of their simplicity, computational efficiency and solid security foundation on a well-studied hard problemâlearning parity with noise. By far, the most efficient protocol is HB#, which is provably resistant to the GRS attack under the conjecture that it is secure in the DET-model. However, in order to achieve 80-bit security, a typical HB# authentication key comprises over 1000 bits, which imposes considerable storage burdens on resource-constrained devices. In this study, the authors propose a new HB-like protocol: HBîą. The protocol makes use of a special type of circulant matrix, in contrast to the Toeplitz matrix in HB#, to significantly reduce storage consumption and overcome a subtle security proof inefficacy in HB#. In addition, the authors introduce a masking technique that substantially increases noise level from an adversary's standpoint, and thus improves protocol performance. The authors demonstrate that 613-bit authentication key suffices for 80-bit security in the HBîą protocol, which is quite competitive and more appealing for low-cost devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frameâs 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".