Quantum-Resistant Identity Management via ZK-STARKs and Decentralized Storage
Bibliographic record
Abstract
Traditional identity management systems are inherently vulnerable to critical issues, including pervasive privacy breaches and single points of failure, which compromise the security and integrity of sensitive user information.These centralized models require the disclosure of personal data to third parties, thereby increasing the risk of misuse, exploitation, and large-scale data leaks.To address these limitations alongside the emerging threat posed by quantum computing this paper proposes a novel identity management architecture that integrates Zero-Knowledge Scalable Transparent Arguments of Knowledge, the InterPlanetary File System, and blockchain technology.This design enables decentralized, privacy-preserving identity verification by allowing users to prove specific identity attributes without revealing the underlying sensitive data, and without the need for trusted third parties.The use of the InterPlanetary File System ensures that encrypted user data is stored off-chain in a distributed, immutable manner, reducing exposure risks and enhancing availability.A functional prototype of the system was developed using a Zero-Knowledge Scalable Transparent Arguments of Knowledge cryptographic library and evaluated to demonstrate its practical feasibility.The evaluation confirms that the architecture is efficient, scalable, and resistant to quantum attacks, making it a strong candidate for real-world digital identity systems.This work provides a forward-looking, secure, and privacy-preserving alternative to traditional identity frameworks.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| 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 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".