Blockchain-Orchestrated Privacy Preservation for Cloud-Based Data Mining
Bibliographic record
Abstract
Data mining on the cloud makes it possible to perform analytics at a large scale using distributed computing resources, but data decentralization makes sensitive data vulnerable to non-trusted or semi-trusted conditions. Traditional security measures are not always sufficient to handle more complicated adversarial threats like data poisoning, model inversion, and unauthorized exploitation. The paper introduces a privacy preservation framework (BOPPF) designed using Blockchain as an orchestration layer; it combines a form of differential privacy, secure aggregation and optional homomorphic encryption. The blockchain guarantees immutable logging, access control using smart contracts and verifiable compliance tracking whereas the defence component utilises anomaly detection and strong aggregation to reduce interference by adversaries. The experimental analysis of MIMIC-III, Credit Card Fraud data and CIFAR-10 data can prove that the framework is highly utility, highly robust, and has a high degree of privacy protection with a low amount of computation overhead. The paper also addresses the concepts of scalability as more nodes are involved and the cost of energy analysis, with the practicality of implementing it being through containerized microservices. In general, the results confirm that blockchain can be used to improve transparency, trust, and auditability of secure cloud data mining processes that can be applied to various industries, including health, finance, and e-commerce.
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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 | 0.006 |
| 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 | 0.003 |
| 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".