Self-Sovereign Identity Architecture for National Use with Wallet Proofs Zero-Knowledge and the VWR Framework
Abstract
National identity must deliver fast, fair decisions without exposing personal data. This article advances a Self-Sovereign Identity (SSI) architecture, reinforced by a Verify-Without-Reveal (VWR) framework, to achieve that goal at national scale. SSI places credentials in a citizen wallet and enables selective disclosure and zero-knowledge proofs, so services can verify attributes without seeing underlying records. VWR adds the policy and accountability spine: yes/no attribute APIs for holder-absent cases, purpose-bound and zero-trust enforcement on every call, and an immutable audit layer on a permissioned ledger. The study synthesises current standards and leading implementations in Europe and worldwide and formulates a deployable blueprint with clear roles, consent and lawful-override flows, per-agency pseudonyms, and regulator and citizen visibility. It details reference APIs, wallet and verifier user experience, and performance targets compatible with national workloads. Privacy-preserving AI strengthens biometric liveness, fraud detection, and anomaly response without centralising sensitive data. The framework aligns with GDPR data minimisation and purpose limitation, supports the European Digital Identity Wallet, and meets high-risk AI governance requirements. Results show how SSI proofs and VWR controls reduce unconsented disclosure and cross-agency browsing, while keeping latency low and interoperability high. The contribution is both conceptual and operational: a phased migration path that turns verify-without-reveal into the default mode for government and regulated services, improving security, inclusion, and public trust.
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References
2. Arner, D. W., Barberis, J. N., & Buckley, R. P. (2020). The identity challenge in finance: From KYC to digital identity. Asia Pacific Law Review, 28(2), 257–275.
3. Camenisch, J., & Lehmann, A. (2021). Privacy-preserving attribute-based credentials and zero-knowledge proofs: A survey and outlook. Foundations and Trends® in Privacy and Security, 4(1), 1–104.
4. Campbell, B., & Weitzner, D. J. (2022). Policy as code for data protection: Operationalising legal rules in information systems. IEEE Security & Privacy, 20(6), 39–50.
5. Dunphy, P., & Petitcolas, F. A. P. (2020). Decentralized digital identity and verifiable credentials: The basics and beyond. IEEE Security & Privacy, 18(4), 16–27.
6. European Commission. (2024). European Digital Identity Framework (eIDAS 2.0) and European Digital Identity Wallet Regulation and implementing measures. Publications Office of the European Union.
7. European Commission. (2024). Artificial Intelligence Act Rules on AI systems and obligations for high-risk use cases. Publications Office of the European Union.
8. Gencer, A. E., & Basu, S. (2021). Tamper-evident audit trails with permissioned ledgers: Design patterns and pitfalls. ACM Queue, 19(5), 1–21.
9. ISO/IEC. (2021). ISO/IEC 18013-5:2021 Personal identification ISO-compliant driving licence Part 5: Mobile driving licence (mDL) application. International Organization for Standardization.
10. Juels, A., & Oprea, A. (2020). New directions in tamper-evident logging. Communications of the ACM, 63(4), 38–47.
11. Kaul, S. (2021). Encrypted inference in practice: Trusted execution and homomorphic approaches for privacy-preserving analytics. IEEE Computer, 54(12), 30–41.
12. Khovratovich, D., & Law, J. (2020). BBS+ signatures Unlinkable selective disclosure in practice. IACR Cryptology ePrint Archive, 2020/1416.
13. Meylan, C., & Sabadello, M. (2021). Decentralized identifiers and verifiable credentials for e-government services. In A. De Santis (Ed.), Digital Identity Management (pp. 121–146). Springer.
14. NIST. (2020). Digital Identity Guidelines (SP 800-63 Rev. 3, including 2020 updates). National Institute of Standards and Technology.
15. Paredes-García, W., Marcel, S., Galbally, J., & Fierrez, J. (2023). Face anti-spoofing and liveness detection: A comprehensive survey. IEEE Transactions on Information Forensics and Security, 18, 1234–1267.
16. Preukschat, A., & Reed, D. (2021). Self-Sovereign Identity: Decentralized digital identity and verifiable credentials. Manning.
17. Ren, X., Li, T., Kairouz, P., & McMahan, H. B. (2025). Privacy-preserving federated learning at scale: Systems, robustness, and governance. Foundations and Trends® in Machine Learning, 18(1), 1–189.
18. Sporny, M., Longley, D., & Chadwick, D. W. (2022). Verifiable Credentials Data Model 2.0. W3C Recommendation.
19. Sporny, M., Burnett, D., & Sabadello, M. (2022). Decentralized Identifiers (DID) Core. W3C Recommendation.
20. Troncoso, C., Isaakidis, M., Danezis, G., & Halpin, H. (2020). Systematizing decentralization and privacy: Lessons from cryptographic designs. Proceedings on Privacy Enhancing Technologies (PoPETs), 2020(4), 307–329.
21. UIDAI. (2025). Aadhaar Authentication and e-KYC Specifications and best practices (Ver. 3.x). Unique Identification Authority of India.
22. Vukolić, M. (2021). Permissioned blockchains: Design goals, consensus choices, and performance trade-offs. Communications of the ACM, 64(12), 38–45.
23. Wagner, I., & Eckhoff, D. (2020). Technical privacy metrics: A systematic survey. ACM Computing Surveys, 54(1), 1–38.
24. Zhou, Y., Xu, Y., & Lyu, M. R. (2021). Byzantine-robust federated learning: A comprehensive survey. IEEE Transactions on Big Data, 7(1), 1–20.
Copyright (c) 2025 Md Abul Mansur
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