A Scan-Based Side Channel Attack on the NTRUEncrypt Cryptosystem
Bibliographic record
Abstract
Scan-based Design-for-Test (DFT) is a widely deployed technique for testing hardware chips. Using this approach, all flip-flops in the design under test are connected to a scan chain where their states can be scanned out through this chain during the testing phase. Scan-based side channel attacks exploit the information obtained by analyzing the scanned data in order to retrieve secret information from cryptographic hardware devices that are designed with this testability feature. The NTRU encryption algorithm (NTRUEncrypt) is a parameterized family of lattice-based public key cryptosystems which has recently been accepted to the IEEE P1363 standards under the specifications for lattice-based public-key cryptography. In this paper, we present a scan-based side channel attack on NTRUEncrypt hardware implementations that employ scan based DFT techniques. Our attack determines the scan chain structure of the polynomial multiplication circuits used in the decryption algorithm which allows the cryptanalyst to efficiently retrieve the secret key.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".