Abstract
The medical service provider establishes a heart failure prediction model with deep learning technology to provide remote users with real-time and accurate heart failure prediction services. Remote users provide their health data to the health care provider for heart failure prediction through the network, thereby effectively avoiding the damage or death of vital organs of the patient due to the onset of acute heart failure. Obviously, sharing personal health data in the exposed data sharing environment would lead to serious privacy leakage. Therefore, in this paper, we propose a privacy-preserving heart failure prediction (PHFP) system based on Secure Multiparty Computation (SMC) and Gated Recurrent Unit (GRU). To meet the real-time requirements of the PHFP system, we designed a series of data interaction protocols based on additional secret sharing to achieve lightweight outsourcing computing. Through these protocols, we can protect the user’s health data privacy while ensuring the efficiency of the heart failure prediction model. At the same time, to provide high-quality heart failure prediction services, we also use the new mathematical fitting method to directly construct the safety activation function, which reduces the number of calls to the security protocol and optimizes the accuracy and efficiency of the system. Besides, we built a security model and analyzed the security of the system. The experimental results show that PHFP takes into account the safety, accuracy, and efficiency in the application of heart failure prediction.
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Acknowledgment
This research is supported by the key project of Anhui provincial department of education (Grant No. KJ2018A0031), the National Natural Science Foundation of China under Grant Nos. U1804263 and 61702105.
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Ying, Z., Cao, S., Zhou, P., Zhang, S., Liu, X. (2020). Lightweight Outsourced Privacy-Preserving Heart Failure Prediction Based on GRU. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_45
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