Blockchain-Driven Supply Chain Visibility with .Net and Azure Confidential Ledger: Design and Implementation Strategies
Bibliographic record
Abstract
The growing demand for transparency and trust in supply chain systems has accelerated the adoption of decentralized technologies capable of delivering tamper-proof and verifiable transaction histories. This paper presents a blockchain-integrated framework built on .NET and Azure Confidential Ledger to enhance supply chain visibility and traceability across distributed logistics networks. The proposed system leverages the immutability of blockchain and the confidentiality guarantees of trusted execution environments to securely record and validate every logistical event—from procurement to final delivery—without exposing sensitive operational data. By integrating Azure Confidential Ledger with ASP.NET Core microservices, the framework ensures secure data logging and access control while maintaining compatibility with enterprise-grade identity and authorization mechanisms. Data is ingested through RESTful APIs and processed using Entity Framework Core for transactional integrity. A modular architecture allows easy extension into existing logistics platforms while providing real-time dashboards and alerting via SignalR and Power BI. Experimental evaluation demonstrates the system’s efficiency in handling concurrent events, maintaining low latency, and preventing unauthorized data modifications. This study offers a scalable and privacy-preserving design pattern for organizations aiming to modernize supply chain management using blockchain technology in secure cloud environments, establishing a foundation for future innovations in decentralized logistics infrastructure
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".