A High Throughput and Secure Authentication-Encryption AES-CCM Algorithm on Asynchronous Multicore Processor
Bibliographic record
Abstract
We propose an authentication-based matrix-transformation cum parallel-encryption implemented on an asynchronous multicore processor (AMP-MP) to achieve a high throughput and yet secure advanced encryption standard based on counter with chaining mode (AES-CCM). There are four main features in our proposed AMP-MP. First, we employ the matrix multiplication in GF(28) computation to transform the 16 plaintexts into one plaintext, hence improving the authentication speed by 32× collectively at the transmitter and receiver. Second, we reschedule the operations of three AES encryptions in three different cores such that their physical leakages are compensated and equalized, thus reducing the correlation of physical leakage with the processed data by >3×. Third, the intermediate values of AES-CCM are propagated asynchronously between different cores to randomize the physical leakages with the processed data, and therefore further enhance the security of AES-CCM against the SCA by another 3×. Fourth, we propose a key adjusting technique based on S-Box byte-key transformation to protect the key against pattern-based attack. Our proposed AMP-MP is realized on an 8-bit asynchronous 9-core processor fabricated based on the 65 nm CMOS process. The experimental results show that the throughput of the authentication is 13.54 Gbps while the throughput for both authentication and encryption collectively is 8.32 Gbps, which are 17× and 70× faster than the reported counterparty, respectively. Based on power dissipation and EM SCA on our proposed AMP-MP, the secret key is unrevealed at 5 × 105 traces, which is ~17× more secured than the standard ASIC AES-CCM implementation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".