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June 1, 2026Frontiers in Blockchain0 citationsOpen Access

ZkHybridChain: ultra-efficient cross-border credit recognition

AXA. J. XuBWB. M. WangCZC. Y. Zhu

Key Points

  • The aim is to develop a framework to improve cross-border credit recognition in Sino-Foreign Cooperative Education while addressing privacy and efficiency concerns.
  • Proposed a dual-layer blockchain credit bank framework (ZkHybridChain) that combines Polygon zkEVM and Hyperledger Fabric.
  • Utilized Zero-Knowledge Proofs and three-tiered smart contracts to enhance credit conversion and verification.
  • Analyzed 10,000 SFCE records to measure efficiency improvements and processing times.
  • Achieved 58% efficiency gain, reducing the full lifecycle from approximately 1,200 seconds to under 9 seconds.
  • Maintained a cross-border latency of under 9 seconds and a throughput of up to 1,620 transactions per second.
  • ZKP verification showed a latency of 135 milliseconds with a 93.7% success rate.

Abstract

Background Cross-border credit recognition in Sino-Foreign Cooperative Education (SFCE) suffers from data fragmentation, regulatory conflicts (e.g., GDPR vs. China’s Data Security Law), and low efficiency. Objective This paper proposes ZkHybridChain, a dual-layer blockchain credit bank (BCB) framework to resolve the privacy-compliance-efficiency trilemma. Methods The hybrid architecture integrates Polygon zkEVM (public credential hashing) and Hyperledger Fabric (private raw data storage). Zero-Knowledge Proofs (ZKP) and three-tiered smart contracts enable automated credit conversion (ECTS↔CNQF) and privacy-preserving verification. Results Experiments on 10,000 SFCE records show 58% efficiency gain (full lifecycle from ∼1,200 s to 9 s), cross-border latency 9 s, throughput up to 1,620 TPS, and ZKP verification latency 135 ms (93.7% success rate). Conclusion ZkHybridChain provides a scalable, GDPR/DSL-compliant solution for global education trust networks. Future work includes post-quantum cryptography and lightweight client protocols.

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Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a1d20f302fbce91306371e8https://doi.org/10.3389/fbloc.2026.1759813
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