Decentralized, Self-Sovereign, Consortium: The Future of Digital Identity in Canada
Bibliographic record
Abstract
This article introduces how SecureKey Technologies Inc. (SecureKey) worked with various network participants and innovation partners alongside government, corporate, and consumer-focused collaborators, in a consortium approach to create a mutually beneficial network of self-sovereign identity (SSI) principles with blockchain in Canada. These principles are based on giving users ownership and control over all of their digital identity attributes as an alternative approach to the current status quo of centralized digital identity, which focuses on discrete identities are made within individual online properties. Blockchain is used as the foundation for its strong security protocols to prevent information from being identified, accessed, or misused and uphold SSI principles. This article will consider the current status quo of digital identity known as centralized digital identity and comparisons to the case study’s emphasis on the alternative thinking of SSI with principles with blockchain, which prioritizes a decentralized, self-sovereign, consortium approach as opposed to discrete identities within individual online properties. Each of these principles will be explained in detail before highlighting the practical implications, lessons learned for future applications, and how both the Canadian and global identity landscapes should proceed for wider acceptance of SSI with blockchain. The case study detailed – that of Verified.Me – will demonstrate how blockchain developers can actively work to help partners transition from current identity silos to instead collaborate across varied industries and create a cohesive, secure service and digital identity network that benefits users through SSI principles and the benefits of blockchain. We also offer recommendations for how both the Canadian and global identity landscapes should proceed for wider acceptance of SSI with blockchain, the benefits of doing so, and anticipated barriers affecting the adoption of future decentralized identity initiatives.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".