Java Benchmark Performance of Homomorphic Polynomial Public Key Cryptography for Key Encapsulation and Digital Signature
Bibliographic record
Abstract
In this paper, we present a comprehensive benchmarking analysis of Homomorphic Polynomial Public Key (HPPK) cryptography, focusing on its Key Encapsulation Mechanism (KEM) and Digital Signature (DS) implementations in Java. Leveraging high-level language implementations, we showcase the outstanding performance of HPPK, demonstrating clock cycles approximately doubled in comparison to its C counterparts. This significant achievement positions HPPK as a versatile and high-performance cryptographic solution, paving the way for extensive applications across various domains. Our study builds upon earlier benchmarking endeavors in C, where Kuang et al. reported exceptional results using the Supercop Toolkit. By transitioning to Java, a high-level language, we highlight the adaptability and efficiency of HPPK, making it accessible for a broader range of applications. The observed doubling of clock cycles in Java implementations underscores the remarkable performance achievable with high-level languages, reinforcing HPPK's standing as a robust and efficient post-quantum cryptographic solution. Through meticulous examination and comparison of key cryptographic operations, including key generation, encapsulation, decapsulation, signing, and verification, our paper provides valuable insights into the practical viability of HPPK in Java. The implications extend to diverse applications such as blockchain, digital currency, and Internet of Things (IoT) devices, where HPPK's superior performance can be harnessed effectively.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".