Enhancing Blockchain Security and Efficiency through FPGA-based Consensus Mechanisms and Post-quantum Cryptography
Bibliographic record
Abstract
Introduction: Blockchain technology has revolutionized data management and transaction recording, extending its application beyond cryptocurrencies to various sectors, including Central Bank Digital Currencies (CBDCs) Methods: This distributed ledger technology offers a transparent, immutable, and secure transaction platform, reducing the risk of data tampering and increasing resistance to attacks. However, challenges such as performance, scalability, and security continue to exist; these challenges are particularly concerning consensus mechanisms like Proof of Work (PoW). Field-Programmable Gate Arrays (FPGAs) present a promising solution to enhance the efficiency and security of blockchain consensus mechanisms. Results: This study explores the implementation of blockchain in embedded systems using FPGAs and discusses the post-quantum cryptographic algorithms to ensure long-term protection. Conclusion: The research highlights the potential of FPGA-based implementations to revolutionize blockchain applications, emphasizing the need for continuous adaptation and vigilance to address evolving security threats, particularly those posed by quantum computing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".