Arithmetical Improvement of the Round-Off for Cryptosystems in High-Dimensional Lattices
Bibliographic record
Abstract
With Lattice-based cryptography (LBC), ciphertexts are represented as points near a lattice, and Babai's round-off algorithm allows to decrypt them when one knows the secret-key. Recently, an accelerated variant of the round-off, based on Residue Number Systems (RNSs), has been proposed. Herein, we combine this technique with the use of lattices of Optimal Hermite Normal Form (OHNF) and propose further refinements, so as to reduce the decryption complexity. This approach lends itself largely to data-level parallelism, allowing for low latency decryption operations on multi-core CPUs with Single Instruction Multiple Data (SIMD) extensions, and achieves high-throughput on GPUs. Finally, we are able to perform decryptions up to 20 times faster than the most efficient implementation in related art, which exploits the Mixed-Radix System (MRS), in an Intel i7 6700K CPU, and we are able to decrypt up to 11,832 messages/s in a Titan X GPU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".