Privacy-Preserving Post-Quantum Credentials for Digital Payments
Bibliographic record
Abstract
Digital payments and decentralized systems enable the creation of new financial products and services for users. One core challenge in digital payments is the need to protect users from fraud and abuse while retaining privacy in individual transactions. We propose a pseudonymous credential scheme for use in payment systems to tackle this problem. The scheme is privacy-preserving, efficient for practical applications, and hardened against quantum computing attacks. We present a constant-round, interactive, zero-knowledge proof of knowledge (ZKPOK) that relies on a one-way function and an asymmetric encryption primitive—both of which need to support at most one homomorphic addition. The scheme is implemented with SWIFFT as a post-quantum one-way function and ring learning with errors as a post-quantum asymmetric encryption primitive, with the protocol deriving its quantum-hardness from the properties of the underlying primitives. We evaluate the performance of the ZKPOK instantiated with the chosen primitive and find that a memory footprint of 85 KB is needed to achieve 200 bits of security. Comparison reveals that our scheme is more efficient than equivalent, state-of-the-art post-quantum schemes. A practical and interactive credential mechanism was constructed from the proposed building blocks, in which users are issued pseudonymous credentials against their personally identifiable information that can be used to register with financial service providers without revealing personal information. The protocol is shown to be secure and free of information leakage, preserving the user’s privacy regardless of the number of registrations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".