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Investigating How Quantum Cryptographic Techniques Can Enhance the Security of Blockchain-Based Artificial Intelligence (AI) Models

Afroja Akther et al.

Journal of Knowledge Management Practice2025https://doi.org/10.62477/jkmp.v25i4.535article
ABDC C
Weight
0.50

What the paper says

The integration of blockchain and artificial intelligence (AI) has revolutionized secure, transparent, and decentralized applications. However, the security of blockchain-based AI models remains reliant on classical cryptographic techniques, such as RSA and ECC, which are increasingly vulnerable to emerging quantum computing threats. This study investigates how quantum cryptographic techniques can enhance the security and resilience of blockchain-based AI applications. Specifically, it explores the role of Quantum Key Distribution (QKD) in securing key exchanges, post-quantum cryptographic (PQC) algorithms in fortifying data encryption, and quantum hashing techniques in protecting blockchain consensus mechanisms. The research evaluates the feasibility, implementation challenges, and performance implications of quantum-enhanced security frameworks for AI-driven blockchain networks. By addressing these concerns, this study establishes a foundation for developing quantum-secured blockchain infrastructures that safeguard AI transactions against future quantum threats while ensuring trust, transparency, and scalability in decentralized applications.

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Cite this paper

https://doi.org/https://doi.org/10.62477/jkmp.v25i4.535

Or copy a formatted citation

@article{afroja2025,
  title        = {{Investigating How Quantum Cryptographic Techniques Can Enhance the Security of Blockchain-Based Artificial Intelligence (AI) Models}},
  author       = {Afroja Akther et al.},
  journal      = {Journal of Knowledge Management Practice},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.62477/jkmp.v25i4.535},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.