Deep neural network-based security awareness research for private data cloud storage

Hui Li; Mei Zhang; Chuanchao Hu

doi:10.1117/12.2668071

20 April 2023 Deep neural network-based security awareness research for private data cloud storage

Hui Li, Mei Zhang, Chuanchao Hu

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260231 (2023) https://doi.org/10.1117/12.2668071
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

In the context of the increasing volume of privacy data, the requirements for storage security are also increasing. Some privacy data cloud storage security-aware methods suffer from excessive storage overhead, which limits the subsequent utilization of space resources. To this end, a deep neural network-based security-aware method for privacy data cloud storage is designed. Identify proof of attribution for cloud storage services, complete read and write data operations on the blockchain with the help of smart contracts, set up privacy data encryption, decryption and authentication mechanisms, hide cryptographic statistics, build a data chunking encoding model based on deep neural networks, combine storage addresses provided by cloud servers, and optimise security-aware models using attack prediction algorithms. Experimental results: The average value of storage overhead of this designed privacy data cloud storage security-aware method is: 6050 KB, which is 460 KB and 487 KB less than the other two respectively, indicating that this designed privacy data cloud storage security-aware method is better when combined with deep neural network.

Citation Download Citation

Hui Li, Mei Zhang, and Chuanchao Hu "Deep neural network-based security awareness research for private data cloud storage", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260231 (20 April 2023); https://doi.org/10.1117/12.2668071

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