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Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

Author

Listed:
  • Gaurav Dhiman

    (Department of Computer Science, Government Bikram College of Commerce, Patiala 147001, India
    University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali 140413, India)

  • Sapna Juneja

    (Department of Computer Science, KIET Group of Institutions, Delhi NCR, Ghaziabad 201206, India)

  • Hamidreza Mohafez

    (Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Ibrahim El-Bayoumy

    (Public Health and Community Medicine, Tanta Faculty of Medicine, Tanta 31527, Egypt)

  • Lokesh Kumar Sharma

    (School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India)

  • Maryam Hadizadeh

    (Centre for Sport and Exercise Sciences, Universiti Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia)

  • Mohammad Aminul Islam

    (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia)

  • Wattana Viriyasitavat

    (Department of Statistics, Chulalongkorn University, Bangkok 10330, Thailand)

  • Mayeen Uddin Khandaker

    (Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Malaysia)

Abstract

The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

Suggested Citation

  • Gaurav Dhiman & Sapna Juneja & Hamidreza Mohafez & Ibrahim El-Bayoumy & Lokesh Kumar Sharma & Maryam Hadizadeh & Mohammad Aminul Islam & Wattana Viriyasitavat & Mayeen Uddin Khandaker, 2022. "Federated Learning Approach to Protect Healthcare Data over Big Data Scenario," Sustainability, MDPI, vol. 14(5), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2500-:d:755691
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    References listed on IDEAS

    as
    1. Abhinav Juneja & Sapna Juneja & Sehajpreet Kaur & Vivek Kumar, 2021. "Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 11(2), pages 38-52, April.
    2. Gaurav Dhiman & Gaganpreet Kaur & Mohd Anul Haq & Mohammad Shabaz, 2021. "Requirements for the Optimal Design for the Metasystematic Sustainability of Digital Double-Form Systems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, November.
    Full references (including those not matched with items on IDEAS)

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