Hardware implementations of the WG-5 cipher for passive RFID tags
Bibliographic record
Abstract
This paper presents two versions of a Welch-Gong cipher designed for use in passive RFID tags. The low-cost and low-power requirements for passive RFID tags impose stringent design constraints for the chips used in the tags. The WG5-80(x) cipher operates over the finite field F25, and has an 80-bit secret key and 80-bit initialization vector. WG5-80(x11) is the same as WG5-80(x), but includes a decimation function of x11, which increases the linear complexity at the cost of losing the 1-order resiliency property that is inherent in the WG-transform. Both ciphers can be implemented using parallel LFSRs to provide throughputs ranging from one to twenty-five bits per clock cycle. On a 130 nm fabrication process with a clockspeed of 100 kHz and a throughput of 100 kbps, WG5-80(x) has an area of 1229 GE (gate equivalents) and a power consumption of 0.78 μW. The linear complexity of the cipher is 217. The corresponding numbers for WG5-80(x11) are 1235GE, 0.79 μW, and 222. This paper presents results for a 130 nm and a 180 nm process, and data rates of 100 kbps and 200 kbps. The combined area and power results for the WG5ciphers are approximately 5% better than previous results for low-data-rate ciphers. In addition, WG-ciphers offer mathematically guaranteed randomness and cryptographic properties not provided by other ciphers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.004 |
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".