Modular Blockchain Architecture: Securing Data with Quantum-Safe Encryption
Bibliographic record
Abstract
The development and increased accessibility of quantum computing paradigms will significantly compromise traditional cryptographic protocols. Decentralized data systems, specifically those built to handle concurrent data transactions such as the blockchain, will be vulnerable to quantum attacks - due to the stark disparity in computational strength and speed relative to classical computing systems. Although quantum computing is still in its infancy, its future threat to data security is imperative. This threat is amplified when considering resource-constrained systems that optimize transaction throughput, which is true for Internet of Things (IoT) devices and infrastructures. Thus, this paper proposes a novel architecture for a Dual-Factor Quantum-Safe Blockchain (DFQSB) to ensure latency minimization and fortify against quantum threats. Before proposing the quantum-safe blockchain, we conduct a background review of blockchain, post-quantum cryptography (PQC), and quantum key distribution (QKD). Then, we describe the proposed DFQSB architecture and provide an overview of the platform, workflow, and modularity. Finally, we identify potential algorithms that can work within the architecture, outline limitations, and make recommendations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".