Enhancing Side Channel Attack-Resistance of the STTL Combining Multi-Vt Transistors with Capacitance and Current Paths Counterbalancing
Bibliographic record
Abstract
Differential power analysis (DPA) exploits the difference between the instantaneous power of the circuit arches transitions to stole the state as information aiming to unveil the cryptographic key. Secure triple track logic (STTL) is a circuit-level countermeasure to DPA attacks based on dual-rail precharge logic (DPL). STTL is robust to attacks due to the delay in an insensitive feature that mitigates the logic glitches generated by the different path delays that lead to the logic gate inputs until they stabilize. The main STTL drawback, however, is the asymmetry of the transistor topology. Asymmetry causes unbalanced internal capacitances and different internal paths for the current flow, and DPA exploits it as a source of information leakage. Our work proposes three circuit topologies, combining multi-Vt transistors with a circuit counterbalancing strategy, aiming to improve the STTL DPA attack-resistance. Data encryption standard substitution-box circuit, designed in a TSMC 40 nm CMOS process, is our application case study to evaluate the DPA attack-resistance. Results gathered at the application-level show that our proposals outperform DPA attack-resistance of the prior work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".