A Decentralized Identity-Based Blockchain Solution for Privacy-Preserving Licensing of Individual-Controlled Data to Prevent Unauthorized Secondary Data Usage
Bibliographic record
Abstract
This paper presents a design for a blockchain solution aimed at the prevention of unauthorized secondary use of data. This solution brings together advances from the fields of identity management, confidential computing, and advanced data usage control. In the area of identity management, the solution is aligned with emerging decentralized identity standards: decentralized identifiers (DIDs), DID communication and verifiable credentials (VCs). In respect to confidential computing, the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (FHE) scheme is incorporated with the system to protect the privacy of the individual’s data and prevent unauthorized secondary use when being shared with potential users. In the area of advanced data usage control, the solution leverages the PRIV-DRM solution architecture to derive a novel approach to licensing of data usage to prevent unauthorized secondary usage of data held by individuals. Specifically, our design covers necessary roles in the data-sharing ecosystem: the issuer of personal data, the individual holder of the personal data (i.e., the data subject), a trusted data storage manager, a trusted license distributor, and the data consumer. The proof-of-concept implementation utilizes the decentralized identity framework being developed by the Hyperledger Indy/Aries project. A genomic data licensing use case is evaluated, which shows the feasibility and scalability of the solution.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.002 |
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".