Design Space Exploration of Galois and Fibonacci Configuration Based on Espresso Stream Cipher
Why this work is in the frame
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Bibliographic record
Abstract
Fibonacci and Galois are two different kinds of configurations in stream ciphers. Although many transformations between two configurations have been proposed, there is no sufficient analysis of their FPGA performance. Espresso stream cipher provides an ideal sample to explore such a problem. The 128-bit secret key Espresso is designed in Galois configuration, and there is a Fibonacci-configured Espresso variant proved with the equivalent security level. To fully leverage the efficiency of two configurations, we explore the hardware optimization approaches toward area and throughput, respectively. In short, the FPGA-implemented Fibonacci cipher is more suitable for extremely resource-constrained or high-throughput applications, while the Galois cipher compromises both area and speed. To the best of our knowledge, this is the first work to systematically compare the FPGA performance of cipher configurations under relatively fair cryptographic security. We hope this work can serve as a reference for the cryptography hardware architecture research community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 it