Enhancing IoT Data Security: Using the Blockchain to Boost Data Integrity and Privacy
Bibliographic record
Abstract
The Internet of Things (IoT) is a technology that can connect billions of devices or “things” to other devices (machine to machine) or even to people via an existing infrastructure. IoT applications in real-world scenarios include smart cities, smart houses, connected appliances, shipping, monitoring, smart supply chain management, and smart grids. As the number of devices all over the world is increasing (in all aspects of daily life), huge amounts of data are being produced as a result. New issues are therefore arising from the use and development of current technologies, regarding new applications, regulation, cloud computing, security, and privacy. The blockchain has shown promise in terms of securing and preserving the privacy of users and data, in a decentralized manner. In particular, Hyperledger Fabric v2.x is a new generation of blockchain that is open source and offers versatility, modularity, and performance. In this paper, a blockchain as a service (BaaS) application based on Hyperledger Fabric is presented to address the security and privacy challenges associated with the IoT. A new architecture is introduced to enable this integration, and is developed and deployed, and its performance is analyzed in real-world scenarios. We also propose a new data structure with encryption based on public and private keys for enhanced security and privacy.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".