A New Approach to Client Onboarding Using Self-Sovereign Identity and Distributed Ledger
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing client onboarding and Know Your Customer (KYC) processes are typically slow, expensive and often accomplished in-person. Moreover, the current identity management models in practice deprive users from having complete control over their digital identity data. Users' identity attributes are stored on multiple centralized repositories, which often follow inadequate security policies. In this paper, we take advantage of Hyperledger Indy, a public and permissioned distributed ledger technology (DLT), to develop a digital onboarding framework based on the Self-Sovereign Identity (SSI) principles. With this framework we take a step towards tackling a number of weaknesses in current KYC processes and identity management models, while addressing the requirements associated with SSI, Privacy by Design and European Union's General Data Protection Regulation (GDPR).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it