Spread Spectrum Encryption Architecture SSEA: A New Encryption Architecture for Post Quantum Computing - Design and Analysis.
Bibliographic record
Abstract
The fast development towards building Quantum Computer (QC) increases the consequences of QC attacks and implies high vulnerabilities to symmetric key cipher systems and public key cipher systems. Increasing key length for symmetric key cipher systems to resist QC attacks implies increasing design size of the algorithm which means slow down the algorithm. Inspired from the unpredictability principle, PRNG is added to the architecture of the symmetric key cipher system to add the unpredictability property to choose which algorithm is used and which subkey is used. Spread Spectrum Encryption Architecture (SSEA) is a family of three architectures with high security level and high speed resistant to QC attacks. First, SSEA has two or more encryption algorithms and multiple subkeys at each round of the encryption algorithm. SSEA architecture is used to hide which algorithm is used, to hide which subkey is used and to hide the output of the encrypted ciphertext. Second, SSEA security level is increased as the number of subkeys for each round increased or the number of rounds in the algorithm increased or the number of algorithms increased. This model increases the security level where the output from the PRNG is not on the communication channel and the attacker cannot perform analysis to this output. Third, cryptanalysis cannot take place over SSEA; the only way for the attacker to break SSEA is to establish brute force attack over all of the system possible combinations. Finally, SSEA3 is chosen to be implemented as it has the highest speed, the lowest design size and the highest security level over SSEA1, SSEA2 and AES-256 full rounds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".