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Secure and privacy in healthcare data using quaternion based neural network and encoder-elliptic curve deep neural network with blockchain on the cloud environment

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Abstract

The security and privacy of healthcare data are crucial aspects within the healthcare industry, as accurate diagnoses rely on medical professionals accessing patient healthcare data. Similarly, patients often require access to their data. However, ensuring that sensitive health data is shared securely while prioritizing privacy is essential. This paper proposes an innovative solution called the quaternion based neural network, Advanced Data Security Architecture in Healthcare Environment (ADSAH), which combines Elliptical curve cryptography (ECC) with a blockchain mechanism and a Deep Fuzzy Based Neural Network (DFBNN) to safeguard cloud-stored health data. The proposed approach begins by encoding the input medical data using an encoder and then encrypting the encoded data using ECC techniques. The secret key for encrypting the data is securely stored within a blockchain framework. The key is divided into blocks to enhance security, and the SHA algorithm is employed to identify key events within these blocks. These key events are subsequently stored in a cloud storage system. A modified genetic algorithm is utilized to generate the encryption and decryption key. This algorithm is explicitly tailored to secure healthcare data. Authorized patients or physicians can access medical data using the secret key to decrypt and retrieve the necessary information. The performance of the proposed network is evaluated by considering factors such as time and cost and is compared against existing studies. The evaluation demonstrates notable improvements, including a reduction in the time required for the encryption and decryption process, as well as a decrease in transaction and execution costs when compared to previous research. By incorporating ECC with a blockchain mechanism and DNN, the ADSAH approach offers an advanced solution for ensuring the security and privacy of cloud-stored health data. It provides robust encryption and facilitates efficient and cost-effective access to authorized individuals while safeguarding sensitive health information.

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Authors and Affiliations

  1. Department of CSBS, Thiagarajar College of Engineering, Madurai, India

    P Suganthi

  2. Department of IT, Velammal College of Engineering and Technology, Madurai, India

    R Kavitha

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  1. P Suganthi
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  2. R Kavitha
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Correspondence to P Suganthi.

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Suganthi, P., Kavitha, R. Secure and privacy in healthcare data using quaternion based neural network and encoder-elliptic curve deep neural network with blockchain on the cloud environment. Sādhanā 48, 206 (2023). https://doi.org/10.1007/s12046-023-02249-2

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  • DOI: https://doi.org/10.1007/s12046-023-02249-2

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