Securing RSA Algorithm Against Side Channel Attacks
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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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