Abstract
The rapid development of mobile devices and 5G networks has promoted the widespread use of mobile healthcare, allowing us to check our health status at any time and from any location, as well as facilitating doctors’ analysis of the condition through timely analysis of these data. However, the convenience provided by mobile healthcare has also exposed data security issues. Medical data, for example, is easily leaked, and the collection of patient treatment data lacks standardized management. To address these issues, we designed a mobile medical data security scheme based on Federated Learning and Digital Twin to guarantee medical data security. The scheme consists of a state-aware module and a digital twin module. The state-aware module collects medical data and stores it encrypted to the blockchain, ensuring the security, trustworthiness, and traceability of medical data. The digital twin module uses horizontal federated learning and digital twin to combine data from multiple medical institutions to build a federated model and a patient digital twin model in an iterative process, which solves the problem of “data silos” while ensuring privacy and security, and aids in the improvement of medical solutions and helps improve medical care. We have conducted experiments and evaluations of the techniques adopted in this scheme, verifying the effectiveness and feasibility.
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Acknowledgements
This work was supported in part by the Hainan Provincial Natural Science Foundation of China (621RC508), Henan Key Laboratory of Network Cryptography Technology (LNCT2021-A16), the Science Project of Hainan University (KYQD(ZR)-21075).
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Wang, N., Han, W., Ou, W. (2023). A Novel Security Scheme for Mobile Healthcare in Digital Twin. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_32
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