Practical CCA2-Secure and Masked Ring-LWE Implementation
Bibliographic record
Abstract
During the last years public-key encryption schemes based on the hardness of ring-LWE have gained significant popularity. For real-world security applications assuming strong adversary models, a number of practical issues still need to be addressed. In this work we thus present an instance of ring-LWE encryption that is protected against active attacks (i.e., adaptive chosen-ciphertext attacks) and equipped with countermeasures against side-channel analysis. Our solution is based on a postquantum variant of the Fujisaki-Okamoto (FO) transform combined with provably secure first-order masking. To protect the key and message during decryption, we developed a masked binomial sampler that secures the re-encryption process required by FO. Our work shows that CCA2-secured RLWE-based encryption can be achieved with reasonable performance on constrained devices but also stresses that the required transformation and handling of decryption errors implies a performance overhead that has been overlooked by the community so far. With parameters providing 233 bits of quantum security, our implementation requires 4,176,684 cycles for encryption and 25,640,380 cycles for decryption with masking and hiding countermeasures on a Cortex-M4F. The first-order security of our masked implementation is also practically verified using the non-specific t-test evaluation methodology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".