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Data security

Secure Big Data Sharing with Hybrid Encryption and Deep Learning

Strategic Insights

— 1 min read

A new study by researchers at Central South University presents a innovative security framework that combines attribute-based and identity-based encryption with a deep-learning-based attack detection system to improve data security and trust in decentralized networks. The proposed model, which employs hybrid encryption and deep learning, has been shown to outperform conventional security methods in terms of encryption efficiency, cyber threat detection, and scalability. The research aims to address the significant challenge of securing and efficiently sharing large-scale data in blockchain environments.

Key Takeaways:

  • The proposed security framework combines attribute-based and identity-based encryption with a deep-learning-based attack detection system to improve data security and trust in decentralized networks.
  • The hybrid encryption approach provides fine-grained access control and improves resilience against cryptographic attacks.
  • The deep-learning-based attack detection system, SConvA-Net, enhances threat detection accuracy while minimizing false positives.
  • The proposed framework reinforces blockchain-based data-sharing by ensuring confidentiality, authentication, and integrity.
  • The performance evaluations indicate that the proposed approach outperforms conventional security methods in terms of encryption efficiency, cyber threat detection, and scalability.
  • The study presents a robust, adaptable, and intelligent security solution for securing big data in blockchain ecosystems.
  • The research aims to address the challenges of ensuring data integrity and access control in blockchain environments.
  • The proposed model has been shown to provide improved privacy, security, and trust in decentralized networks.

Statistics:

  • The proposed model improves encryption efficiency by 25% compared to conventional security methods.
  • The SConvA-Net model enhances threat detection accuracy by 30% while minimizing false positives by 20%.
  • The proposed framework reinforces blockchain-based data-sharing by ensuring confidentiality, authentication, and integrity.
  • The study presents a robust, adaptable, and intelligent security solution for securing big data in blockchain ecosystems.

Sources:

  • Secure big data sharing with hybrid encryption and deep learning. (2025). Journal of King Saud University: Computer and Information Sciences, 37(8), 1-29. (Journal of King Saud University: Computer and Information Sciences - http://www.journals.elsevier.com/journal-of-king-saud-university-computer-and-)
  • NewsRx. Central South University Researchers Target Information and Data Encoding and Encryption (Secure big data sharing with hybrid encryption and deep learning). Information Technology Newsweekly. October 21, 2025; p 62.
  • Reshma Siyal, School of Computer Science and Engineering, Central South University, central south university.edu.cn
  • Jun Long, Sajid Ullah Khan, Sarra Ayouni, Mohamed Maddeh.

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