Security and Privacy in Healthcare Applications Using Federated Learning - Review

Authors

  • Amitesh Kumar Singh School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Aishwarya M School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Shishir Shastry BH School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Usharani MP School of Computer Science and Engineering, REVA University, Bengaluru, India

DOI:

https://doi.org/10.59535/sehati.v3i1.371

Keywords:

Federated Learning, Security and Privacy, Healthcare, Metaverse

Abstract

This review explored data security and privacy in Internet of Health Things (IoHT) networks, focusing on local training for AI models and Federated Learning (FL) to ensure privacy and integrity. This highlights the importance of leveraging technological advancements for disease prediction and data exchange. Functional programming can be used in various industries and applications under real-world scenarios. In the medical industry, safeguarding the confidentiality of patient records and medical status is essential. This is where collaborative or federated learning becomes relevant. On the other hand, creating an intelligent system that helps medical personnel without exposing data can result in a Federated Learning concept. An example is an AI-based intelligent system for diagnosing brain tumors, which can effectively operate within a teamwork setting. Advancements in smart devices and applications designed for the Internet of Healthcare Things have created an ideal setting for implementing Machine Learning techniques. Nevertheless, conventional ML solutions work by collecting and processing data in a centralized manner. Federated Learning (FL) offers a potential solution for training machine-learning models on numerous separate devices without the need to share private data. As a result, FL provides a secure framework for managing extremely sensitive data in the context of the IoHT. This survey provides a detailed overview of new data security and privacy tools for Federated Learning in Internet of Health Things networks. Initially, we introduced the fundamental concepts of Federated Learning (FL) as it is applied to the IoHT.

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References

M. A. Ahmad, C. Eckert, and A. Teredesai, "Interpretable machine learning in healthcare," in Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2018, pp. 559–560.

A. Callahan and N. H. Shah, "Machine learning in healthcare," in Key Advances in Clinical Informatics, Elsevier, 2017, pp. 279–291.

I. Y. Chen, E. Pierson, S. Rose, S. Joshi, K. Ferryman, and M. Ghassemi, "Ethical machine learning in healthcare," Annu. Rev. Biomed. Data Sci., vol. 4, pp. 123–144, 2021.

N. Rieke et al., "The future of digital health with federated learning," npj Digit. Med., vol. 3, no. 1, pp. 1–7, 2020.

C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, "A survey on federated learning," Knowl.-Based Syst., vol. 216, 2021, Art. no. 106775.

S. Singh, S. Rathore, O. Alfarraj, A. Tolba, and B. Yoon, "A framework for privacy-preservation of IoT healthcare data using federated learning and blockchain technology," Future Gener. Comput. Syst., vol. 129, pp. 380–388, 2022.

C. A. Da Costa, C. F. Pasluosta, B. Eskofier, D. B. Da Silva, and R. da Rosa Righi, "Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards," Artif. Intell. Med., vol. 89, pp. 61–69, 2018.

L. Wang, Y. Ali, S. Nazir, and M. Niazi, "ISA evaluation framework for security of internet of health things system using AHP-TOPSIS methods," IEEE Access, vol. 8, pp. 152316–152332, 2020.

W. Y. B. Lim et al., "Federated learning in mobile edge networks: A comprehensive survey," IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 2031–2063, 2020.

D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. Vincent Poor, "Federated learning for Internet of Things: A comprehensive survey," IEEE Commun. Surv. Tutor., vol. 23, no. 3, pp. 1622–1658, 2021, doi: 10.1109/COMST.2021.3075439.

L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, "Federated learning for Internet of Things: Recent advances, taxonomy, and open challenges," IEEE Commun. Surv. Tutor., 2021.

M. J. Baucas, P. Spachos, and K. N. Plataniotis, "Federated learning and blockchain-enabled fog-IoT platform for wearables in predictive healthcare," IEEE Trans. Comput. Soc. Syst., 2023.

S. S. Rani et al., "Optimal users based secure data transmission on the internet of healthcare things (IoHT) with lightweight block ciphers," Multimedia Tools Appl., vol. 79, pp. 35405–35424, 2020.

S. Qiao et al., "Sprechd: Four-chamber semantic parsing network for recognizing fetal congenital heart disease in medical metaverse," IEEE J. Biomed. Health Inform., 2022.

Y.-T. Song and J. Qin, "Metaverse and personal healthcare," Procedia Comput. Sci., vol. 210, pp. 189–197, 2022.

S. Chen et al., "Fl-qsar: a federated learning-based qasr prototype for collaborative drug discovery," Bioinformatics, vol. 36, no. 22-23, pp. 5492–5498, 2021.

Q. Dou et al., "Federated deep learning for detecting covid-19 lung abnormalities in ct: a privacy-preserving multinational validation study," NPJ Digit. Med., vol. 4, no. 1, pp. 1–11, 2021.

R. Wang et al., "Privacy preserving federated learning for Internet of Medical Things under edge computing," IEEE J. Biomed. Health Inform., 2022.

D. C. Nguyen et al., "Federated learning for smart healthcare: A survey," ACM Comput. Surv., vol. 55, no. 3, pp. 1–37, 2022.

B. Kitchenham, Procedures for Performing Systematic Reviews, vol. 33, no. 2004, Keele University, Keele, UK, 2004, pp. 1–26.

K. Sahiba and F. O. Catak, "Secure multi-party computation-based privacy preserving data analysis in healthcare IoT systems," 2021, arXiv preprint arXiv:2109.14334.

P. Chavan et al., "Dual-Step Hybrid Mechanism for Energy Efficiency Maximization in Wireless Network," Cybern. Inf. Technol., vol. 23, no. 3, pp. 70–88, Sep. 01, 2023, doi: 10.2478/cait-2023-0025.

H. V. Ramachandra et al., "Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture," J. Electr. Comput. Eng., vol. 2023, Art. ID 9390660, 11 pages, 2023.

P. Kumar D et al., "Adaptive Hybridized Meta-Heuristic Algorithm for Subspace Clustering on High Dimensional Data," J. Adv. Inf. Technol., vol. 14, no. 5, pp. 1103-1116, 2023.

M. Pujar et al., "An Efficient Framework for Web Content Mining Systems Using Improved CD-PAM Clustering and the A-CNN Technique," SN Comput. Sci., vol. 4, no. 5, Sep. 09, 2023, doi: 10.1007/s42979-023-02137-w.

S. G., M. R. Mundada, S. Supreeth, and B. Gardiner, "Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing," Int. J. Comput. Netw. Inf. Secur., vol. 15, no. 4, pp. 84–95, Aug. 08, 2023, doi: 10.5815/ijcnis.2023.04.08.

Y. Desai et al., "Performance Evaluation of Integrated Hard Real-Time Application and RISC V Processor for Spacecraft on Board Software Application," Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 3, pp. 810–817, 2023. Available: https://www.ijisae.org/index.php/IJISAE/article/view/3287.

S. Kumar S et al., "A Deep Learning-Based Approach for Identification and Recognition of Criminals," Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 3, pp. 975–987, 2023. Available: https://ijisae.org/index.php/IJISAE/article/view/3352.

S. Supreeth and K. Patil, "Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms," Int. J. Emerg. Technol. Learn., vol. 17, no. 07, pp. 208–225, 2022, doi: 10.3991/ijet.v17i07.29223.

S. Supreeth and K. Patil, "VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment," KSII Trans. Internet Inf. Syst., vol. 16, no. 6, Jun. 30, 2022, doi: 10.3837/tiis.2022.06.007.

S. Supreeth et al., "An Efficient Policy-Based Scheduling and Allocation of Virtual Machines in Cloud Computing Environment," J. Electr. Comput. Eng., vol. 2022, Art. ID 5889948, 12 pages, 2022, doi: 10.1155/2022/5889948.

Shruthi G, Monica R Mundada, Supreeth S, “Resource Allocation Using Weighted Greedy Knapsack Based Algorithm in an Educational Fog Computing Environment,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 18. International Association of Online Engineering (IAOE), pp. 261–274, Sep. 21, 2022. doi: 10.3991/ijet.v17i18.32363.

G. Shruthi, Monica R. Mundada, B. J. Sowmya, S. Supreeth, "Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing", Applied Computational Intelligence and Soft Computing, vol. 2022, Article ID 2131699, 17 pages, 2022. https://doi.org/10.1155/2022/2131699.

S. Kumara, N. H. Prasad, M. Monika, H. Tuli, S. Supreeth and S. Rohith, "Smart Vehicle Parking System on Fog Computing for Effective Resource Management," 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), Dharwad, India, 2023, pp. 1-6, doi: 10.1109/ICAISC58445.2023.10201108.

Krishnamurthy, K.T., Rohith, S., Basavaraj, G.M., Swathi, S., Supreeth, S. (2023). Design and Development of Walking Monitoring System for Gait Analysis. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science, vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_44

R. S, M. M R, B. G M, S. Bhargavi, S. Supreeth and C. S, "Reversible Data Embedding Scheme In Encrypted Image Using Arnold Transform for Satellite Images," 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Vijaypur, India, 2022, pp. 1-5, doi: 10.1109/NKCon56289.2022.10126927.

S. Supreeth, K. Patil, S. D. Patil and S. Rohith, "Comparative approach for VM Scheduling using Modified Particle Swarm Optimization and Genetic Algorithm in Cloud Computing," 2022 IEEE International Conference on Data Science and Information System (ICDSIS), 2022, pp. 1-6, doi: 10.1109/ICDSIS55133.2022.9915907.

S. Rohith, L. Jahnavi, S. C. Bhuvaneshwari, S. Supreeth and B. K. Sujatha, “Image Encryption and Decryption Using Key Sequence of Triple Logistic Map for Medical Applications,” 2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC), IEEE, 2020, pp. 1-5, doi: 10.1109/ICAECC50550.2020.9339529.

Ranjith.R, Supreeth S, Ramya R, Ganesh prasad. M, Chaitra Lakshmi L, “Password Processing Scheme using Enhanced Visual Cryptography and OCR in Hybrid Cloud Environment”, IJEAT, Volume-8, Issue-5S, May, 2019.

Gaurav Dhingra, Neha K R, Amruthashree R V,Eshitha D, Supreeth S, “Traffic Management Using Convolution Neural Network”, IJEAT, Volume-8, Issue-5S, May, 2019.

Supreeth, S. and Patil, Kiran Kumari (2019). Virtual Machine scheduling strategies in Cloud Computing- A Review. International Journal on Emerging Technologies, 10(3): 181–188, 2019.

Supreeth, S., Raja Rajeshwari, M.M. (2018). Using Map-Reduce for Image Analysis in Cloud Environment. In: Guru, D., Vasudev, T., Chethan, H., Kumar, Y. (eds) Proceedings of International Conference on Cognition and Recognition. Lecture Notes in Networks and Systems, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-10-5146-3_11.

S. Sandroni, “Medical education in the metaverse,” Nature Medicine, pp. 1–2, 2022.

I. Skaldic, O. Muller, and S. Fournier, “Cardio verse: The cardiovascular medicine in the era of metaverse,” Trends in Cardiovascular Medicine, 2022.

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Published

2024-12-20

Issue

Vol. 3 No. 1 (2025): Socio-Economic and Humanistic Aspects for Township and Industry

Section

Review Article

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Copyright (c) 2024 Amitesh Kumar Singh, Aishwarya M, Shishir Shastry BH, Usharani MP

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