A Comparative Study on Post-Quantum Cryptographic Digital Signature Algorithms: Network Performance, Key Robustness, and Energy Consumption.
Bibliographic record
Abstract
The concept of Post Quantum Cryptography (PQC) gains profound importance considering the imminent arrival of quantum computers.PQC involves security techniques that can withstand attacks from both regular and quantum computers.This urgency arises due to the anticipated progress in quantum computing, which poses significant risks to traditional cryptographic methods.As a result, there is a pressing need to swiftly establish PQC solutions to address these potential vulnerabilities.This research delves into various Post Quantum Cryptography (PQC) digital signature algorithms, examining their robustness against brute force attacks, network performance, and energy consumption.Also, the study focuses on MPPK/DS (Multivariate polynomial public key digital signature) algorithm in generating the Python code and further utilizing true random numbers from a quantum computer, secure MPPK/DS key pairs are generated, and their robustness is measured through semi-covariance correlation analysis, revealing MPPK's superior resilience compared to RSA and SPHINCS+.The study further assesses latency performance on 5G, Wi-Fi, and local networks, highlighting efficacy for real-world use.Additionally, the research addresses the energy consumption of all the major PQC NIST (National Institute of Standards and Technology) selected digital signature algorithms, stressing the significance of cryptographic solutions that can work well in conjunction with resourceconstrained upcoming intelligent networks of devices.As we move towards a quantumsafe cryptographic landscape, this work's contributions provide valuable insights for securing the digital realm in the face of emerging quantum threats.The research outcomes and developments are shared openly with the research community to facilitate further comparisons and advancements in the field of PQC algorithms.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".