Arithmetic operators for on-the-fly evaluation of TRNGs
Bibliographic record
Abstract
Many cryptosystems embed a high-quality true random number generator (TRNG). The randomness quality of a TRNG output stream depends on its implementation and may vary due to various changes in the environment such as power supply, temperature, electromagnetic interferences. Attacking TRNGs may be a good solution to decrease the security of a cryptosystem leading to lower security keys or bad padding values for instance. In order to protect TRNGs, on-the-fly evaluation of their randomness quality must be integrated on the chip. In this paper, we present some preliminary results of the FPGA implementation of functional units dedicated to statistical tests for on-the-fly randomness evaluation. In the entropy test the evaluation of the harmonic series at some ranks is required. Usually its approximation is costly. We propose a multiple interval polynomial approximation. The decomposition of the whole domain into small sub-intervals leads to a good trade-off between the degree of the polynomial (i.e. multipliers cost) and the memory resources required to store the coefficients for all sub-intervals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".