<scp>ShareChain</scp>: Blockchain‐enabled model for sharing patient data using federated learning and differential privacy
Bibliographic record
Abstract
Abstract Every individual in our technologically evolved world needs proper data security. The procedure of exchanging medical information is increasingly concerned with data privacy. Many techniques have been offered for preserving data security. These techniques use approaches such as ‐anonymity, ‐diversity, and others. However, such solutions are vulnerable to attribute disclosure, homogeneity, and background knowledge risks due to their syntactic nature. In this work, we describe a safe and secure architecture and semantic approach for data sharing that is based on blockchain, local differential privacy (LDP), and federated learning (FL). The proposed framework generates an atmosphere devoid of trust in which data owners are no longer required to have trust in the controllers. The FL models enable the whole network to decentralize its data‐driven learning. Interplanetary file system (IPFS) is used to provide data security in a distributed environment because each file in IPFS has a digital fingerprint that is computed using a cryptographic hash function on the file's whole contents. Due to the rigorous privacy guarantee, data owners no longer need to be worried about the security of their data. The proposed model's assessment parameters include latency, throughput, privacy, and accuracy. The data privacy of the proposed model is protected via LDP and FL, and its latency and throughput communication transactions on permissioned blockchain are calculated and compared with those of the benchmark model. The findings indicate that the proposed model delivers 85% more accurate privacy than the benchmark model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".