Secure big data sharing with hybrid encryption and deep learning
Securing and efficiently sharing large-scale data is a significant challenge, particularly in blockchain environments where ensuring data integrity and access control is crucial. This study introduces an innovative security framework that combines attribute-based and identity-based encryption (IABHE...
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| Veröffentlicht in: | Journal of King Saud University. Computer and information sciences Jg. 37; H. 8; S. 216 - 29 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing
01.10.2025
Springer Nature B.V Springer |
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| ISSN: | 1319-1578, 2213-1248, 1319-1578 |
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| Abstract | Securing and efficiently sharing large-scale data is a significant challenge, particularly in blockchain environments where ensuring data integrity and access control is crucial. This study introduces an innovative security framework that combines attribute-based and identity-based encryption (IABHE), Honey Encryption, and a deep-learning-based attack detection system (SConvA-Net). The proposed model improves privacy, security, and trust in decentralized networks by employing an advanced encryption mechanism (IABHE + Honey Encryption) that provides fine-grained access control and improves resilience against cryptographic attacks. Furthermore, the SConvA-Net model enhances threat detection accuracy while minimizing false positives. By integrating these technologies, the 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. This study presents a robust, adaptable, and intelligent security solution for securing big data in blockchain ecosystems. |
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| AbstractList | Securing and efficiently sharing large-scale data is a significant challenge, particularly in blockchain environments where ensuring data integrity and access control is crucial. This study introduces an innovative security framework that combines attribute-based and identity-based encryption (IABHE), Honey Encryption, and a deep-learning-based attack detection system (SConvA-Net). The proposed model improves privacy, security, and trust in decentralized networks by employing an advanced encryption mechanism (IABHE + Honey Encryption) that provides fine-grained access control and improves resilience against cryptographic attacks. Furthermore, the SConvA-Net model enhances threat detection accuracy while minimizing false positives. By integrating these technologies, the 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. This study presents a robust, adaptable, and intelligent security solution for securing big data in blockchain ecosystems. Abstract Securing and efficiently sharing large-scale data is a significant challenge, particularly in blockchain environments where ensuring data integrity and access control is crucial. This study introduces an innovative security framework that combines attribute-based and identity-based encryption (IABHE), Honey Encryption, and a deep-learning-based attack detection system (SConvA-Net). The proposed model improves privacy, security, and trust in decentralized networks by employing an advanced encryption mechanism (IABHE + Honey Encryption) that provides fine-grained access control and improves resilience against cryptographic attacks. Furthermore, the SConvA-Net model enhances threat detection accuracy while minimizing false positives. By integrating these technologies, the 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. This study presents a robust, adaptable, and intelligent security solution for securing big data in blockchain ecosystems. |
| ArticleNumber | 216 |
| Author | Khan, Sajid Ullah Ayouni, Sarra Maddeh, Mohamed Long, Jun Siyal, Reshma |
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| SubjectTerms | Access control Accuracy Algorithms Attack detection Big Data Blockchain Blockchain security Collaboration Computer Imaging Computer Science Confidentiality Cryptography Data encryption Data exchange Data integrity Database Management Datasets Deep learning Deep stacked autoencoder Efficiency Encryption Feature selection Information sharing Integrity Machine Learning Multiple data security Neural networks Original Paper Pattern Recognition and Graphics Performance evaluation Privacy Real time Relief method Security Siamese convolutional neural network Software Engineering/Programming and Operating Systems Standard scores Systems and Data Security Theory of Computation Threats Vision |
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| Title | Secure big data sharing with hybrid encryption and deep learning |
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