Bibliographic record
Abstract
NTRU (Nth degree Truncated polynomial Ring Units) is probably the only post quantum public key cryptosystem suitable for practical implementation. Recently, several NTRU based systems have also been shown having property of homomorphic encryption with important application in cloud computing security. In this thesis, several efficient algorithms and architectures for NTRUEcrypt system and for NTRU based homomorphic encryption system are proposed. For NTRUEncrypt system, a new LFSR (linear feedback shift register) based architecture is firstly presented. A novel design of the modular arithmetic unit is proposed to reduce the critical path delay. The FPGA implementation results have shown that the proposed design outperforms all the existing works in terms of area-delay product. Secondly, a new architecture using extended LFSR is proposed for NTRUEncrypt system. It takes advantage of small polynomials with many zero coefficients, and thus significantly reduces the latency of the computation with modest increase of the complexity. Thirdly, a systolic array architecture is proposed for NTRUEncrypt. There is only one type of PE (process element) in the array and the PE was designed with optimized arithmetic. The systolic array yields all the output in N clock cycles. Two new architectures are proposed for computation of NTRU based fully homomorphic encryption system. One architecture uses LFSR with a novel design of the modular multiplication unit, and the other proposed architecture is systolic array based which uses two types of PEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".