A Blockchain-Enabled CMS for Transparency and Data Integrity in E-Commerce Applications
Bibliographic record
Abstract
Content management systems (CMS) underpin large e-commerce platforms, yet conventional deployments prioritize throughput over verifiable integrity. This tradeoff limits adoption in settings that require traceability and resistance to tampering. Extensions such as append-only audit logs and cloud security add-ons mitigate risk to a degree, although they retain centralized control and can increase latency without end-to-end provenance. This work introduces a Blockchain-Enabled CMS (BC-CMS) that uses Hyperledger Fabric for the ledgering of metadata and IPFS for off-chain storage of media. Smart contracts provide access control, provenance and auditability across distributed peers. To test at scale, we create a workload based on the Instacart Online Grocery Shopping Dataset with synthetic data to scale to$\text{1 0 0, 0 0 0}$products, 2.5 M content revisions, and 1.2 M media assets. The benchmark exercises to read, update, create and delete mixes, based on common patterns of operations. BC-CMS has 2940 transactions per second with 95th percentile latency of 214 ms, has 99.996 % tamper detection accuracy, and has availability ¿99.9%. Compared to previous blockchain-based CMSs baseline, the throughput increased about 10 to 15 %, and storage overhead also eliminated by off-chain media management. These results indicate that the proposed design achieves a balance between efficiency and integrity for trust sensitive e-commerce applications. Future directions include multi tenant isolation, adaptive consensus selection and lightweight cryptographic protocols to further improve scalability.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".