Bibliographic record
Abstract
RSA’s modular exponentiation is the basic operation in public key infrastructure and is naturally the target of side-channel attacks. In this work we propose two algorithms that defeat side-channel attacks: Paired Permutation Exponentiation (PPE) and Permute, Split, and Accumulate (PSA). We compare these two algorithms with the classic right-to-left technique. All three implementations are evaluated using Intel® Performance Counter Monitor (PCM) at an effective 0.25 ms sampling interval. We use fixed 2048-bit inputs, pin the Python 3.9.13 process to a single core Intel® Core™ i5-10210U, and repeat each experiment 100 and 1000 times to characterize behavior and ensemble statistics. Our proposed technique PSA shows the lowest runtime and the strongest hardening against per-bit correlation relative to the standard RtL. Residual leakage related to the Hamming weight of the exponent may remain observable but the only information gathered is the the Hamming weight of the secret key. The exact location of the secret key bits is completely obscured.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".