IBS-ECDHE: A blockchain-enhanced lightweight protocol for secure cloud-IoT in biomedical HCPS
Bibliographic record
Abstract
The rapid adoption of cloud-Internet-of-Things (CIoT) systems in biomedical human-cyber-physical systems (HCPS) has raised significant concerns regarding data security, privacy, and scalability. To address these challenges specifically within healthcare environments, we propose a novel IBS-ECDHE framework that integrates Identity-Based Signatures (IBS) and Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) key exchange to provide robust and lightweight security for biomedical HCPS. Our framework leverages blockchain technology to decentralize identity management and access control, ensuring secure authentication and maintaining the integrity of sensitive biomedical data exchanged between IoT-enabled medical devices. By incorporating smart contracts, we automate key management and enforce stringent privacy and data integrity guarantees critical to biomedical applications. The proposed system was implemented on a Windows 10 PC and evaluated using various performance metrics, including authentication time, message size, transaction latency, and computational overhead. Experimental results demonstrate that IBS-ECDHE reduces authentication time by up to 76 % compared to traditional PKI systems, decreases message size by 40 %, and achieves lower blockchain transaction latency. The system also ensures scalability and energy efficiency, with parallel processing reducing latency by 37 %. The innovation of this approach lies in the combination of IBS with ECDHE for mutual authentication and the use of blockchain for decentralized identity management and secure real-time biomedical data exchange. This solution offers substantial improvements in security, privacy, and performance, making it highly suitable for next-generation biomedical HCPS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".