Addressing Audit and Accountability Issues in Self-Sovereign Identity Blockchain Systems Using Archival Science Principles
Bibliographic record
Abstract
Self-sovereign identity (SSI) systems are novel blockchain-based solutions that are said to shift the control of data records from organizations to individuals. Contrary to conventional blockchains, such as Bitcoin or Ethereum, many SSI systems do not capture on ledger the exchange of transactional data between individuals. By not capturing the exchange of transaction data such SSI systems have the advantage of complying with privacy regulations such as the EU’s General Data Protection Regulations, but, at the same time, have the disadvantage of not capturing evidence that an exchange has happened. Such evidence, however, may be needed for audit and accountability purposes. To achieve these objectives and to preserve privacy, we leverage archival principles to introduce a novel concept of a proof registry, which we define as a set of technical components, data structures, and process flows, that assures that authoritative records offering evidence of transactions is captured, stored, and accessible. This solution solves the compliance and accountability problem while preserving the self-sovereignty and privacy of involved parties.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| 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".