A Novel Approach to Enhance the Security and Efficiency of Binary Ring-LWE for IoT Resource-Constrained
Bibliographic record
Abstract
The rapid expansion of the Internet of Things (IoT) brings a vast proliferation of network connections. This surge in connectivity significantly increases the risk of private data exposure during transmission and processing. Traditional public key encryption schemes face considerable challenges due to their high computational complexity and vulnerability to quantum attacks. Recently, Lattice-based cryptography, particularly the Binary Ring Learning With Errors (BRLWE) paradigm, has garnered significant attention for its quantum resistance and lightweight computational requirements. However, BRLWE remains vulnerable to physical attacks, especially Side-Channel Attacks (SCA). This paper proposes a novel 3-Decomposition Karatsuba multiplication-based random shuffling scheme to enhance both the efficiency and security of BRLWE. We evaluate the security performance of our proposed scheme against quantum hybrid attacks and SCAs. We assess the performances of different Karatsuba multiplication techniques in terms of computation cost, energy consumption and memory usage to make choose which Karatsuba technique is suitable for our proposal. Our experimental results show that our proposed approach provides the lowest encryption computation time of 18.97 ms and decryption computation time of 9.53 ms compared to the BRLWE and its improved versions. Furthermore, it improves the security level while it decreases the computation time of the original BRLWE by 32.49% and 20.58%, for the encryption and decryption phases, respectively.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".