Design of secure cryptography against the threat of power-attacks in DSP-embedded processors
Bibliographic record
Abstract
Embedded wireless devices require secure high-performance cryptography in addition to low-cost and low-energy dissipation. This paper presents for the first time a design methodology for security on a VLIW complex DSP-embedded processor core. Elliptic curve cryptography is used to demonstrate the design for security methodology. Results are verified with real dynamic power measurements and show that compared to previous research a 79% improvement in performance is achieved. Modification of power traces are performed to resist simple power analysis attack with up to 39% overhead in performance, up to 49% overheads in energy dissipation, and up to 11% overhead in code size. Simple power analysis on the VLIW DSP core is shown to be more correlated to routine ordering than individual instructions. For the first time, differential power analysis results on a VLIW using real power measurements are presented. Results show that the processor instruction level parallelism and large bus size contribute in making differential power analysis attacks extremely difficult. This research is important for industry since efficient yet secure cryptography is crucial for wireless communication devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".