Federated learning enabled multi-key homomorphic encryption
The rapid advancement of digital healthcare systems has significantly increased the volume of medical data, raising concerns about its security and confidentiality. As patient information is highly sensitive, any compromise could have severe consequences in healthcare delivery. Federated Learning (F...
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| Published in: | Expert systems with applications Vol. 268; p. 126197 |
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| Format: | Journal Article |
| Language: | English |
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05.04.2025
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| ISSN: | 0957-4174 |
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| Abstract | The rapid advancement of digital healthcare systems has significantly increased the volume of medical data, raising concerns about its security and confidentiality. As patient information is highly sensitive, any compromise could have severe consequences in healthcare delivery. Federated Learning (FL) has emerged as a promising solution by enabling decentralized data processing through local training and global model aggregation. Despite its advantages, FL faces challenges in ensuring the secure transmission of data and preserving privacy during model updates. Homomorphic encryption (HE)-based security is a promising approach for secure data transmission. Hence, the Puzzle Archimedes Optimization Algorithm (PAOA)-based multi-key HE is designed in this research. The healthcare data is obtained from the dataset and shared among many parties using a multi-party data-sharing platform. All parties utilize noise-free HE to protect sensitive data and enhance communication security. The ideal multi-keys are created using the proposed PAOA. Moreover, the PAOA-trained deep residual network (DRN) (PAOA_DRN) is utilized for privacy data classification. The outcome from each of the local training is aggregated into the global training, and the weights of every local training model are updated. Furthermore, the training accuracy, training loss, mean average precision, and Mean Squared Error (MSE) metrics are utilized for computing the efficacy of the PAOA_DRN. On the Cleveland dataset, the model achieved superior results with a training accuracy of 0.946, training loss of 0.053, MAP of 0.951, and MSE of 0.182. This research provides a significant step forward in addressing critical issues in healthcare data security, with the potential for broad application in medical data sharing and privacy-preserving analytics. |
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| AbstractList | The rapid advancement of digital healthcare systems has significantly increased the volume of medical data, raising concerns about its security and confidentiality. As patient information is highly sensitive, any compromise could have severe consequences in healthcare delivery. Federated Learning (FL) has emerged as a promising solution by enabling decentralized data processing through local training and global model aggregation. Despite its advantages, FL faces challenges in ensuring the secure transmission of data and preserving privacy during model updates. Homomorphic encryption (HE)-based security is a promising approach for secure data transmission. Hence, the Puzzle Archimedes Optimization Algorithm (PAOA)-based multi-key HE is designed in this research. The healthcare data is obtained from the dataset and shared among many parties using a multi-party data-sharing platform. All parties utilize noise-free HE to protect sensitive data and enhance communication security. The ideal multi-keys are created using the proposed PAOA. Moreover, the PAOA-trained deep residual network (DRN) (PAOA_DRN) is utilized for privacy data classification. The outcome from each of the local training is aggregated into the global training, and the weights of every local training model are updated. Furthermore, the training accuracy, training loss, mean average precision, and Mean Squared Error (MSE) metrics are utilized for computing the efficacy of the PAOA_DRN. On the Cleveland dataset, the model achieved superior results with a training accuracy of 0.946, training loss of 0.053, MAP of 0.951, and MSE of 0.182. This research provides a significant step forward in addressing critical issues in healthcare data security, with the potential for broad application in medical data sharing and privacy-preserving analytics. |
| ArticleNumber | 126197 |
| Author | Kumbhar, Hemant Ramdas Srinivasa Rao, S. |
| Author_xml | – sequence: 1 givenname: Hemant Ramdas orcidid: 0009-0004-9956-1197 surname: Kumbhar fullname: Kumbhar, Hemant Ramdas email: hemant.kumbhar@gmail.com – sequence: 2 givenname: S. surname: Srinivasa Rao fullname: Srinivasa Rao, S. email: srinu1479cse@kluniversity.in |
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| Cites_doi | 10.1007/s00607-020-00847-0 10.3390/cryptography6030034 10.1109/ACCESS.2021.3114581 10.1007/978-3-319-72395-2_49 10.1016/j.future.2020.04.034 10.4018/IJISMD.2020070102 10.3389/fcvm.2023.1117360 10.1155/2020/3910250 10.1016/j.procs.2018.10.199 10.1002/int.22818 10.3390/app9061207 10.1007/s10489-020-01893-z 10.1007/s11227-021-03720-9 10.1145/3412357 10.1016/j.neucom.2019.11.041 10.3390/math10244678 10.1109/JIOT.2021.3066307 10.3390/s22155574 |
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| Keywords | Homomorphic Encryption (HE) Deep Residual Network (DRN) Archimedes Optimization Algorithm (AOA) Puzzle Optimization Algorithm (POA) Healthcare Data |
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prediction in modern healthcare systems publication-title: Sensors doi: 10.3390/s22155574 – ident: 10.1016/j.eswa.2024.126197_b0025 – volume: 15 issue: 1 year: 2022 ident: 10.1016/j.eswa.2024.126197_b0120 article-title: POA: Puzzle optimization algorithm publication-title: International Journal of Intelligent Engineering & Systems |
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