A Privacy-Enhanced Multiarea Task Allocation Strategy for Healthcare 4.0

The continuous development of Healthcare 4.0 has brought great convenience to people. Through the Internet of Things technology, doctors can analyze patients' health data and make timely diagnosis. However, behind the high efficiency, the mobile crowdsensing technology used for data transmission still has the risk of leaking the privacy of task and patient information. To this end, this article proposes a privacy-enhanced multi-area task assignment strategy, named PMTA. Specifically, we use deep differential privacy to add noise to patient data, and then put the noise-added dataset into a deep Q-network for training, combined with a spectral clustering algorithm, to obtain an optimal classification strategy. Further, in order to address the problem of data silos, we adopt federated learning to jointly train the classification models of different hospitals to obtain a global model and realize data sharing among different hospitals. Finally, we use the optimal classification of patients for task deployment on the blockchain, and limit patients to only apply for tasks of the corresponding level through the smart contract technology, so as to protect task privacy. Experimental results show that our strategy can not only effectively protect task and patient privacy, but also achieve better system performance.

Automated summary

The continuous development of Healthcare 4.0 has brought great convenience to people by enabling doctors to analyze patients' health data and make timely diagnoses through IoT technology. However, the mobile crowdsensing technology used for data transmission still poses risks of privacy leakage. In response to this issue, this article proposes a privacy-enhanced multi-area task assignment strategy called PMTA. By incorporating deep differential privacy, a noise is added to patient data and fed into a deep Q-network for training, combined with spectral clustering for optimal classification. Federated learning is employed to jointly train classification models across different hospitals, allowing for data sharing and addressing data silos. The optimal patient classification is deployed on the blockchain using smart contract technology, ensuring task privacy. Experimental results demonstrate that this strategy effectively protects task and patient privacy while improving system performance.