The Simeck Family of Lightweight Block Ciphers.
Bibliographic record
Abstract
Abstract. Two lightweight block cipher families, Simon and Speck, have been proposed by researchers from the NSA recently. In this paper, we introduce Simeck, a new family of lightweight block ciphers that com-bines the good design components from both Simon and Speck, in order to devise even more compact and efficient block ciphers. For Simeck32/64, we can achieve 505 GEs (before the Place and Route phase) and 549 GEs (after the Place and Route phase), with the power consumption of 0.417 µW in CMOS 130nm ASIC, and 454 GEs (before the Place and Route phase) and 488 GEs (after the Place and Route phase), with the power consumption of 1.292 µW in CMOS 65nm ASIC. Furthermore, all of the instances of Simeck are smaller than the ones of hardware-optimized cipher Simon in terms of area and power consumption in both CMOS 130nm and CMOS 65nm techniques. In addition, we also give the secu-rity evaluation of Simeck with respect to many traditional cryptanalysis methods, including differential attacks, linear attacks, impossible differ-ential attacks, meet-in-the-middle attacks, and slide attacks. Overall, all of the instances of Simeck can satisfy the area, power, and throughput requirements in passive RFID tags.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".