A power analysis resistant FPGA implementation of NTRUEncrypt
Bibliographic record
Abstract
NTRUEncrypt is a family of public key cryptosystems that uses lattice-based cryptography. It has been accepted as an IEEE P1363 standard and as an X9.98 Standard. In addition to its small footprint compared to other number theory based public key systems, its resistance to quantum attacks makes it a very attractive candidate for post quantum cryptography systems. On the other hand, similar to other cryptographic schemes, unprotected hardware implementations of NTRUEncrypt are susceptible to side channel attacks such as timing and power analysis. In this paper, we present an FPGA implementation of NTRUEncrypt which is resistant to first order differential power analysis (DPA) attacks. Our countermeasures are implemented at the architecture level. In particular, we split the ciphertext into two randomly generated shares. This guarantees that during the first step of the decryption process, the inputs to the convolution modules, which are convoluted with the secret key polynomial, are uniformly chosen random polynomials which are freshly generated for each convolution operation and are not under the control of the attacker. The two shares are then processed in parallel without explicitly combining them until the final stage of the decryption. Furthermore, during the final stage of the decryption, we also split the used secret key polynomial into two randomly generated shares which provides theoretical resistance against the considered class of power analysis attacks. The proposed architecture is implemented using Altera Cyclone IV FPGA and simulated on Quartus II in order to compare the non-masked architecture with the masked one. For the considered set of parameters, the area overhead of the protected implementation is about 60% while the latency overhead is between 1.4% to 6.9%.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".