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Privacy-preserving design for emergency response scheduling system in medical social networks

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Abstract

Mobile health monitoring, which has been expected as an effective way to improve medical service quality, can monitor the medical users’ real-time physiology parameters and the Emergency Response Support System (ERSS) is built up to deal with emergencies. Unfortunately, the information privacy of both the medical users and the healthcare service providers is at risk when their private information is uploaded to the health center. Especially, when emergencies happen, the privacy must be protected and the responding time of the system should not be compromised. In order to protect the privacy and also maintain high efficiency when an emergency occurs, this paper proposes a privacy-preserving based scheduling scheme for emergency response (PPSSER) system. The system can protect physiological data privacy, location privacy and personal attribute privacy. Also it can defend collusion attack. The simulation demonstrates the effectiveness and feasibility of the proposed scheme.

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Acknowledgments

This work was supported in part by the National Basic Research Program of China under Grant 2010CB731803, in part by the NSF of China under Grants U1405251, 61221003, 61290322, 61174127, and 61273181, in part by the Ministry of Education of China under Grants NCET 13-0358, 20110073130005, and 20110073120025, and in part by the Science and Technology Commission of Shanghai Municipality (STCSM), China under Grant 13QA1401900 and 14511107903, sponsored by Medical and Engineering Interdiscipline Project under Grant YG2013ZD0504 in Shanghai Jiao Tong University.

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Wenbin Yu, Zhe Liu, Cailian Chen, Bo Yang & Xinping Guan

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  1. Wenbin Yu
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  2. Zhe Liu
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  3. Cailian Chen
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  4. Bo Yang
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  5. Xinping Guan
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Correspondence to Wenbin Yu.

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Yu, W., Liu, Z., Chen, C. et al. Privacy-preserving design for emergency response scheduling system in medical social networks. Peer-to-Peer Netw. Appl. 10, 340–356 (2017). https://doi.org/10.1007/s12083-016-0429-4

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