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Improving scalability and security medical dataset using recurrent neural network and blockchain technology

Publication Type : Conference Paper

Publisher : IEEE

Source : 2021 International Conference on System, Computation, Automation and Networking (ICSCAN)

Url : https://ieeexplore.ieee.org/abstract/document/9526531

Campus : Coimbatore

Center : TIFAC CORE in Cyber Security

Department : TIFAC-CORE in Cyber Security

Year : 2021

Abstract : Nowadays, dealing with medical data is very difficult task. Because medical dataset contains very sensible details. And also handling of this crucial data is another difficult task. While dealing and handling with such data’s, data leakage problem may occur which paves the way to unstable the cloud environment. Existing approaches enables blockchain technology over cloud environment by processing large amounts of raw data which in turn reduces the overall network performance. In order to overcome these issues, we suggest a blockchain technology combined with Recurrent Neural Networks. The main objectives of the proposed system are two folded. Initially, we classify the data into high priority and low priority using Recurrent Neural Network (RNN) to improve the performance and scalability. Second, the blockchain technology is applied over high priority data to enhance the security of the sensitive information. Then, the low priority data is stored into a separate log file for later use. Comparison results have shown that the proposed system outperforms existing systems.

Cite this Research Publication : R Nilaiswariya, J Manikandan, P Hemalatha " Improving scalability and security medical dataset using recurrent neural network and blockchain technology", 2021 International Conference on System, Computation, Automation and Networking (ICSCAN)

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