A simplified approach for designing secure Random Number Generators in HW
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a method to design a Random Number Generator (RNG), which is a fundamental element in cryptographic and other security related systems. The proposed RNG implementation is based on a Gollmann cascade of Filtered Feedback with Carry Shift Register (FFCSR) cores and is suitable for a wide range of applications. In order to comply with the demands of most applications the RNG must have low hardware cost and power dissipation, and be suitable for real time operation while maintaining a high level of security. In the proposed solution, elementary F-FCSR components are modularly combined to fit the RNG for the desirable application. The RNG will produce a pseudo-random sequence with suitable period, linear complexity and statistical quality. Simulations performed using the statistical test suite available through NIST, show that the proposed RNG holds good statistical properties, a secure mathematical structure and meets known standards.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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