RFID tags authentication by unique hash sequence detection
Bibliographic record
Abstract
With the rise of internet of things an immense number of RFID tags will be associated with different systems that require not only strong authentication protocols, but also time- and power- efficient protocols to authenticate more tags in a given time window. In current tag authentication protocols, a tag is considered authentic if the interrogators find a match to the tag's encrypted (e.g., using some hashing function) reply in the system's database. Tree-based authentication protocols provide rapid authentication by limiting the searched keys at the interrogator from O(N) to O(log(N)), where N is the number of leaves in the balanced tree. However, if one tag is compromised in such protocols, other tags will be at risk of being compromised. In this paper we propose Unique Hash Sequence Authentication (UHSA) protocol. The protocol utilizes tag-interrogator interaction, with a continuous wave (CW) sensor at the tag to cut off tags encrypted reply when the received bits are enough to determine next node in the tree without receiving the whole reply. Cutting off the encrypted reply limits the information that can be obtained by the adversary to compromise the tag. In addition, the reduction in tag reply length greatly enhances the time and power efficiency of the RFID system during the authentication process by more than 90% when compared to existing authentication protocols.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".