Performance Evaluation of Multi-Organization E-Government Based on Hyperledger Fabric Blockchain Platform
Bibliographic record
Abstract
E-governments can face various security problems due to the nature of online systems and the sensitive information they handle.Some of these security issues include: cyberattacks, data breaches, Identity theft, access control, and many others.Permissioned blockchain is one of the cutting-edge technologies that can help to reduce the impact of security vulnerabilities of e-governments.In this work, six organizations as a case study have adopted, two scenarios performed to analyze the performance of hyperledger fabric (a permissioned blockchain platform).In these two scenarios, several parameters have changed, including: transaction send rates, block sizes, number of organizations, and number of clients.The organizations have added gradually to conclude the effect of multiorganization on the throughput, latency, and scalability of the system.Good throughput and latency results were obtained by increase the block size to about 100 transactions per block.Within three to four organizations, the throughput and latency results were accepted, but adding more organizations, the throughput and latency have been affected negatively.Also, it has concluded that with few users in the system, the throughput and latency were reasonably good.Still, with adding more clients, the throughput decreased, and the latency increased rapidly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".