How to improve security and reduce hardware demands of the WIPR RFID protocol
Bibliographic record
Abstract
In this paper, we analyze and improve WIPR, an RFID identification scheme based on public key techniques with efficient hardware implementation. First we analyze the security and privacy features of WIPR. We show that a reduced version of WIPR is vulnerable to short padding attacks and WIPR needs a random number generator with certain properties to withstand reset attacks. We discuss countermeasures to avoid these attacks. Then we propose two variants of WIPR, namely WIPR-SAEP and WIPR-RNS, to improve its security and to further reduce its hardware cost. Using an additional hash function, WIPR-SAEP achieves provable security in the sense that violating the security properties leads to solving the integer factoring problem. WIPR-RNS uses a residue number system (RNS) for computation, and reduces the hardware costs of WIPR. WIPR-RNS provides a better security guarantee than WIPR in that it does not use a non-standard cryptographic primitive in WIPR. WIPR-SAEP and WIPR-RNS can be combined into one scheme.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".