GDPR Compliant Data Storage and Sharing in Smart Healthcare System: A Blockchain-Based Solution
Bibliographic record
Abstract
Smart healthcare systems provide user-centric medical services to patients based on collected information of patients inducing personal health information (PHI) and personal identifiable information (PII). The information (PII and PHI) flows into the smart healthcare system with or without any regulation and patient concern with the help of new information and communication technologies (ICT). The use of ICT comes with the security and privacy issues of collected PII and PHI data. The Europe Union has published the General Data Protection Regulation (GDPR) to regulate the flow of personal information. Towards this end, this paper proposes a blockchain-based data storage and sharing framework for a smart healthcare system that complies with the “Privacy by Design” rule of the GDPR. The personal information collected from patients is stored on off-chain storage (IPFS), and other information is stored on the blockchain ledger, which is visible to all participants. The smart contracts are designed to share the PII data with another participant based on prior permission of the data owner. The proposed framework also includes the deletion of PII and PHI in the system as per the “Right to be Forgotten” GDPR rule. Security and privacy analyses are performed for the framework to demonstrate the security and privacy of data while sharing and at rest. The comparative performance analysis demonstrates the benefit of the proposed GDPR-compliant data storage and sharing framework using blockchain. It is evident from the reported results that the proposed framework outperforms the state-of-the-art techniques in terms of performance metrics in a smart healthcare system.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".