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
Hauptverfasser:	Siyal, Reshma, Long, Jun, Khan, Sajid Ullah, Ayouni, Sarra, Maddeh, Mohamed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.10.2025 Springer Nature B.V Springer
Schlagworte:	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 Attack detection Blockchain security Deep stacked autoencoder Z-score normalization Siamese convolutional neural network Multiple data security Relief method
ISSN:	1319-1578, 2213-1248, 1319-1578
Online-Zugang:	Volltext
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