Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System
Abstract
In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system. Cryptographic primitives, including masks and homomorphic encryption, are applied for further protecting local models, so as to prevent the adversary from inferring private medical data by various attacks such as model reconstruction attack or model inversion attack, etc. The qualities of the datasets owned by different participants are considered as the main factor for measuring the contribution rate of the local model to the global model in each training epoch, instead of the size of datasets commonly used in deep learning. A dropout-tolerable scheme is proposed in which the process of federated learning would not be terminated if the number of online clients is not less than a preset threshold. Through the analysis of the security, it shows that the proposed scheme satisfies data privacy. Computation cost and communication cost are also analyzed theoretically. Finally, skin lesion classification using training images provided by the HAM10000 medical dataset is set as an example of healthcare applications. Experimental results show that compared with existing schemes, the proposed scheme obtained promising results while ensuring privacy preserving.
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SMC relies on cryptographic techniques such as secret sharing and homomorphic encryption to ensure input confidentiality during computation.
“SMC relies on cryptographic techniques such as secret sharing [129, 133] and homomorphic encryption [6, 134] to ensure input confidentiality during computation.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [6]
Hybrid RS architectures can be implemented by combining multiple approaches, such as incorporating secure multiparty computation into a FedRec structure, to maintain strict data privacy.
“Also, to maintain strict data privacy, hybrid RS architectures can be implemented by combining multiple approaches, such as incorporating secure multiparty computation into a FedRec structure [6].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [6]
Cryptosystems like Paillier and ElGamal are commonly utilized in PPRS to implement PHE for privacy preservation.
“Cryptosystems like Paillier and ElGamal are commonly utilized in PPRS to implement PHE for privacy preservation [6, 17, 147, 148].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [6]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Shamir, A. (1979) · Communications of the ACM · doi:10.1145/359168.359176 · Cited by 13,756 · see citing works
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Federated learning of predictive models from federated Electronic Health Records
Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.007 · Cited by 997 · see citing works
Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., & Thorne, B. (2017) · arXiv · doi:10.48550/arxiv.1711.10677 · Cited by 431 · see citing works
Experiments of Federated Learning for COVID-19 Chest X-ray Images
Yan, B., Wang, J., Cheng, J., Zhou, Y., Zhang, Y., Yang, Y., Liu, L., Zhao, H., Wang, C., & Liu, B. (2021) · Communications in Computer and Information Science · doi:10.1007/978-3-030-78618-2_4 · Cited by 122 · see citing works
Privacy-preserving and communication-efficient federated learning in Internet of Things
Fang, C., Guo, Y., Hu, Y., Ma, B., Feng, L., & Yin, A. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102199 · Cited by 93 · see citing works
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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works
Federated learning for healthcare applications
Chaddad, A., Wu, Y., & Desrosiers, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325822 · Cited by 136 · see citing works
Mantey, E. A., Zhou, C., Anajemba, J. H., Arthur, J. K., Hamid, Y., Chowhan, A., & Otuu, O. O. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3350232 · Cited by 75 · see citing works
Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey
Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51 · see citing works
Privacy-Preserving Federated Learning with Resource Adaptive Compression for Edge Devices
Hidayat, M. A., Nakamura, Y., & Arakawa, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3347552 · Cited by 29 · see citing works
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Zhang, L., Xu, J., Vijayakumar, P., Sharma, P. K., & Ghosh, U.
- Affiliations
- Hunan University of Science and Technology (CN)
- Department of Computer Science and Engineering, University College of Engineering Tindivanam, Tindivanam, Tamilnadu, India
- University of Aberdeen (GB)
- Meharry Medical College (US)
- Venue
- IEEE Transactions on Network Science and Engineering
- Published
- 2023-09-01
- Volume
- 10
- Issue
- 5
- Pages
- 2864-2880
- ISSN
- 2327-4697 (ISSN-L)
- 2334-329X
- OpenAlex
- W4285196740
- Cited by
- 376
- References
- 10
- Citations by year
- 2026: 69 · 2025: 137 · 2024: 114 · 2023: 51 · 2022: 5
- FWCI
- 25.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Network Science and Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China (61872138)
- Natural Science Foundation of Hunan Province (2021JJ30278)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- homomorphic encryption, privacy-preserving federated learning, Internet of Medical Things, model inversion attacks, data privacy, dataset quality, skin lesion classification, HAM10000, medical data protection
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