Blockchain-based federated identity and auditing
Bibliographic record
Abstract
A federated identity is a single identity that enables users to access multiple services across a network of business parties. Such identities are subject to various threats and attacks and face diverse challenges including identity leaks, centralised management, auditing limitations, and long breach investigation processes. This paper proposes a framework aimed at automating and decentralising the generation and auditing of a robust and secured blockchain-based federated identity in a marketplace. Business parties participating in the marketplace form the nodes of a distributed blockchain network and participate in the creation of federated identities. Users of this network can access services provided by any one of the participating parties using a single federated identity. All transactions are fully audited in the blockchain, meaning that participating parties can monitor access to their service and users can trace the use of their identities. The proposed framework has been evaluated using two blockchain technologies (Ethereum and Hyperledger Fabric) to measure its performance in public and permissioned blockchain environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".