A Blockchain-Based Cryptographic Framework for Secure, Private, and Traceable Digital Art Copyright Management

Jiang Lv & Jiuru Lin

International Journal of Information Security and Privacy2026https://doi.org/10.4018/ijisp.401345article
ABDC C
Weight
0.50

What the paper says

Digital art growth brings copyright challenges: slow verification, tampering risks, and privacy leaks. This study proposes a blockchain-based cryptographic framework for secure, private, and traceable management. The layered design uses AES for content encryption, ECC for key security, and blockchain for immutable metadata storage, ensuring integrity and authenticity. Smart contracts enable automated access control with pseudonymized identities to protect user privacy. Tested on 2D, 3D, and dynamic artworks, the system outperforms traditional DRM: verification latency drops over 50%, tamper detection exceeds 95%, and CPU/memory usage stays low. It supports scalable, real-time operations and provides an auditable, trust-aware environment for content lifecycle management. Though interoperability and new formats remain challenges, the framework meets technical, organizational, and regulatory needs in information security. This work advances integrated, privacy-preserving solutions for sustainable digital rights ecosystems.

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https://doi.org/https://doi.org/10.4018/ijisp.401345

Or copy a formatted citation

@article{jiang2026,
  title        = {{A Blockchain-Based Cryptographic Framework for Secure, Private, and Traceable Digital Art Copyright Management}},
  author       = {Jiang Lv & Jiuru Lin},
  journal      = {International Journal of Information Security and Privacy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisp.401345},
}

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A Blockchain-Based Cryptographic Framework for Secure, Private, and Traceable Digital Art Copyright Management

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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.