Implementation of a decoupling based power analysis attack countermeasure
Bibliographic record
Abstract
This study presents new prototyped evaluation results for the authors’ proposed power analysis attack countermeasure architecture based on decoupling individual sensitive modules with low current consumption. The proposed architecture includes a switch box module to randomise internal connections, mixing residual information that may leak through non‐ideal switch elements and uneven charge cycles. The two implementations evaluated are a printed circuit board (PCB) developed using stand‐alone CMOS components and the post‐layout simulation of a circuit developed in 0.18 µm TSMC CMOS technology using Cadence. Both systems were able to protect a decoupled 8‐bit XOR module from a correlation power analysis performed using traces collected at the power supply rail for at least 8000 plaintext inputs. The results show that the countermeasure is suitable for both on‐chip and on‐board designs. Analysis of the measurements collected from the PCB test system demonstrates the need to balance the charge/discharge frequency of the decoupling elements against the operational frequency of the decoupled modules. From the layout, an individual decoupling element was found to be similar in size to the decoupled 8‐bit XOR module, with all four decoupling elements occupying a total of 51% of the layout area. This percentage is expected to decrease in the context of larger, more complex systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".