Research on medical data security sharing scheme based on homomorphic encryption
With the deep integration of "AI + medicine", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security se...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023106?viewType=HTML |
| _version_ | 1811175558373965824 |
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| author | Lihong Guo Weilei Gao Ye Cao Xu Lai |
| author_facet | Lihong Guo Weilei Gao Ye Cao Xu Lai |
| author_sort | Lihong Guo |
| collection | DOAJ |
| description | With the deep integration of "AI + medicine", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security seriously hinder data sharing among medical institutions. To fully exploit the value of medical data and realize data collaborative sharing, we developed a medical data security sharing scheme based on the C/S communication mode and constructed a federated learning architecture that uses homomorphic encryption technology to protect training parameters. Here, we chose the Paillier algorithm to realize the additive homomorphism to protect the training parameters. Clients do not need to share local data, but only upload the trained model parameters to the server. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and weights, aggregating the local model parameters from the clients and predicting the joint diagnostic results. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updating and transmitting the trained model parameters back to the server. In order to test the performance of this scheme, a series of experiments was conducted. From the simulation results, we can know that the model prediction accuracy is related to the global training rounds, learning rate, batch size, privacy budget parameters etc. The results show that this scheme realizes data sharing while protecting data privacy, completes the accurate prediction of diseases and has a good performance. |
| first_indexed | 2024-04-10T19:37:44Z |
| format | Article |
| id | doaj.art-7c0ad61d29b7420c8a2544c547d40a5c |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T19:37:44Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-7c0ad61d29b7420c8a2544c547d40a5c2023-01-30T01:00:03ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-01-012022261227910.3934/mbe.2023106Research on medical data security sharing scheme based on homomorphic encryptionLihong Guo0Weilei Gao1Ye Cao2Xu Lai3Department of Information and Communications Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaDepartment of Information and Communications Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaDepartment of Information and Communications Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaDepartment of Information and Communications Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaWith the deep integration of "AI + medicine", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security seriously hinder data sharing among medical institutions. To fully exploit the value of medical data and realize data collaborative sharing, we developed a medical data security sharing scheme based on the C/S communication mode and constructed a federated learning architecture that uses homomorphic encryption technology to protect training parameters. Here, we chose the Paillier algorithm to realize the additive homomorphism to protect the training parameters. Clients do not need to share local data, but only upload the trained model parameters to the server. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and weights, aggregating the local model parameters from the clients and predicting the joint diagnostic results. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updating and transmitting the trained model parameters back to the server. In order to test the performance of this scheme, a series of experiments was conducted. From the simulation results, we can know that the model prediction accuracy is related to the global training rounds, learning rate, batch size, privacy budget parameters etc. The results show that this scheme realizes data sharing while protecting data privacy, completes the accurate prediction of diseases and has a good performance.https://www.aimspress.com/article/doi/10.3934/mbe.2023106?viewType=HTMLdata security sharingfederated learninghomomorphic encryptionmodelalgorithms |
| spellingShingle | Lihong Guo Weilei Gao Ye Cao Xu Lai Research on medical data security sharing scheme based on homomorphic encryption Mathematical Biosciences and Engineering data security sharing federated learning homomorphic encryption model algorithms |
| title | Research on medical data security sharing scheme based on homomorphic encryption |
| title_full | Research on medical data security sharing scheme based on homomorphic encryption |
| title_fullStr | Research on medical data security sharing scheme based on homomorphic encryption |
| title_full_unstemmed | Research on medical data security sharing scheme based on homomorphic encryption |
| title_short | Research on medical data security sharing scheme based on homomorphic encryption |
| title_sort | research on medical data security sharing scheme based on homomorphic encryption |
| topic | data security sharing federated learning homomorphic encryption model algorithms |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023106?viewType=HTML |
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