Publication: Blockchain–AI–Geolocation Integrated Architecture for Mobile Identity and OTP Verification
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Article
Date
2025-11-23
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Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Abstract
One-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators.
Description
Keywords
OTP, SIM swap detection, geolocation authentication, artificial intelligence, blockchain identity
Citation
Sulochana, G.G.D.; De Silva, D.I. Blockchain–AI–Geolocation Integrated Architecture for Mobile Identity and OTP Verification. Future Internet 2025, 17, 534. https://doi.org/10.3390/fi17120534
