Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

Authors

  • N.V. Rajasekhar Reddy MLR Institute of Technology
  • Swathi Baswaraju New Horizon College of Engineering
  • P. Mary Kamala Kumari Lakireddy Bali Reddy College of Engineering, Mylavaram
  • Phanikanth Chintamaneni Koneru Lakshmaiah Education Foundation, Guntur
  • B. Raveendra Naick Mohan Babu University, Tirupati
  • B. Gunapriya Pradhan New Horizon College of Engineering

DOI:

https://doi.org/10.62110/sciencein.jist.2024.v12.805

Keywords:

Patient Data, Data Security, Inception-ResnetV2, Cryptographic Techniques, Internet of Things (IoT)

Abstract

The IoT is transforming healthcare by enabling extensive connectivity between medical professionals, equipment, staff, and patients, facilitating real-time monitoring. While the network's scale and diversity offer advantages for data exchange, they also pose challenges for privacy and security, particularly with sensitive medical information. To address this, deep learning-based cryptographic and biometric systems are utilized for authentication and anomaly detection in medical systems. However, power constraints on network sensors necessitate efficient security schemes. Thus, the authors propose a novel framework, the deep Inception-ResNetV2 with privacy preservation, to secure data transmission while minimizing encryption and decryption time. Implementing this method reduces the network's burden, saving time and costs in communication. Compared to alternatives like private biometric-based authentication, this model demonstrates superior performance.

URN:NBN:sciencein.jist.2024.v12.805

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Author Biographies

  • N.V. Rajasekhar Reddy, MLR Institute of Technology

    Department of Information Technology

  • P. Mary Kamala Kumari, Lakireddy Bali Reddy College of Engineering, Mylavaram

    Department of Computer Science and Engineering

  • Phanikanth Chintamaneni, Koneru Lakshmaiah Education Foundation, Guntur

    Department of Computer Science and Engineering

  • B. Raveendra Naick, Mohan Babu University, Tirupati

    Department of AI & ML, School of Computing

patient data securing

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Published

2024-02-07

Issue

Section

Computer Sciences and Mathematics

URN

How to Cite

Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things. (2024). Journal of Integrated Science and Technology, 12(5), 805. https://doi.org/10.62110/sciencein.jist.2024.v12.805

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