An Efficient Paillier Homomorphic Encryption Circuit With Optional CRT Acceleration for IoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Paillier scheme, widely recognized as the most prevalent additive homomorphic encryption paradigm, faces significant challenges in Internet of Things (IoT) applications due to latency, power, and hardware overhead. This paper proposes an efficient Paillier homomorphic encryption circuit for IoT, integrating Chinese Remainder Theorem (CRT) acceleration. First, we propose an algorithm framework tailored for hardware reuse that supports multiple functionalities of the Paillier scheme. It introduces an Montgomery modular multiplication (MMM) algorithm with superior Area-Time Product (ATP) to implement core computations, and reduces hardware cost by reusing MMM to replace other computational units. Then, a computational unit reuse architecture based on the algorithmic framework is designed to reduce resource overhead. Moreover, a split-coupled MMM circuit design is proposed to counteract computational resource expansion induced by CRT operations. The hardware design is synthesized under SMIC 40 nm CMOS technology. The evaluation shows that the proposed scheme provides a high-performance Paillier circuit design with less area and lower power, offering an effective solution for data security processing in IoT.
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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 | 0.000 | 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 it