FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare System
محورهای موضوعی : Data Mining
Nasibeh Mohammadi
1
,
Afshin Rezakhani
2
,
Hamid Haj Seyyed Javadi
3
,
Parvaneh asghari
4
1 - Department of Computer Engineering, Islamic Azad University, Boroujerd Branch, Borujerd, Iran
2 - Department of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran
3 - Department of Computer engineering, Shahed University, Tehran, Iran
4 - Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Healthcare System, History-based Access control, Intelligent Module, Deep Recurrent Networks, Federated Learning ,
چکیده مقاله :
Giving access permission based on histories of access is now one of the security needs in healthcare systems. However, current access control systems are unable to review all access histories online to provide access permission. As a result, this study first proposes a method to perform access control in healthcare systems in real time based on access histories and the decision of the suggested intelligent module. The data is used to train the intelligent module using the LSTM time series machine learning model. Medical data, on the other hand, cannot be obtained from separate systems and trained using different machine-learning models due to the sensitivity and privacy of medical records. As a result, the suggested solution employs the federated learning architecture, which remotely performs machine learning algorithms on healthcare systems and aggregates the knowledge gathered in the servers in the second phase. Based on the experiences of all healthcare systems, the servers communicate the learning aggregation back to the systems to control access to resources. The experimental results reveal that the accuracy of history-based access control in local healthcare systems before the application of the suggested method is lower than the accuracy of the access control in these systems after aggregating training with federated learning architecture.
Giving access permission based on histories of access is now one of the security needs in healthcare systems. However, current access control systems are unable to review all access histories online to provide access permission. As a result, this study first proposes a method to perform access control in healthcare systems in real time based on access histories and the decision of the suggested intelligent module. The data is used to train the intelligent module using the LSTM time series machine learning model. Medical data, on the other hand, cannot be obtained from separate systems and trained using different machine-learning models due to the sensitivity and privacy of medical records. As a result, the suggested solution employs the federated learning architecture, which remotely performs machine learning algorithms on healthcare systems and aggregates the knowledge gathered in the servers in the second phase. Based on the experiences of all healthcare systems, the servers communicate the learning aggregation back to the systems to control access to resources. The experimental results reveal that the accuracy of history-based access control in local healthcare systems before the application of the suggested method is lower than the accuracy of the access control in these systems after aggregating training with federated learning architecture.
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