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GTSIM-POP: Game theory based secure incentive mechanism and patient-optimized privacy-preserving packet forwarding scheme in m-healthcare social networks

机译:GTSIM-POP:基于博弈论的安全激励机制和移动医疗社交网络中患者优化的隐私保护分组转发方案

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摘要

M-healthcare social networks are becoming increasingly important in supporting an efficient and promising e-healthcare platform. The moving patients suffering from the same disease or living in the neighborhood of the same healthcare provider constitute a delay tolerant network (DTN) to help each other forwarding the patient health information (PHI). Unfortunately, the mobile device carried on each patient collecting PHI from body sensors is subjected to sophisticated attacks. To reduce the probability of patients being threatened by a newly identified target node compromise attack, maximize the credits earned from transmitting packets and achieve the fairness among patients, a game theory based secure incentive mechanism GTSIM is proposed. Based on it, by exploiting the complete marriage theory in combinatorics, a patient-optimized privacy-preserving packet forwarding scheme POP is devised to protect the PHI confidentiality and the identity/location privacy of the patients, decrease the privacy exposure when intermediate patients are compromised and realize the property of patient-optimization required in m-healthcare systems. Last but not least, the security analysis shows both our proposed GTSIM and POP can resist various sophisticated attacks and the extensive simulation demonstrates the practicability and efficiency with high message delivery ratio, obtainable utility fraction and low average latency. (C) 2019 Elsevier B.V. All rights reserved.
机译:移动医疗社交网络在支持高效且有前途的电子医疗平台方面变得越来越重要。患有相同疾病或居住在同一医疗保健提供者附近的移动患者构成了延迟耐受网络(DTN),以帮助彼此转发患者健康信息(PHI)。不幸的是,每个从身体传感器收集PHI的患者身上携带的移动设备都遭受了复杂的攻击。为了降低患者受到新近识别出的目标节点威胁攻击的威胁,最大化传输数据包所获得的信用并实现患者之间的公平性,提出了一种基于博弈论的安全激励机制GTSIM。在此基础上,通过结合组合学中的完整婚姻理论,设计了一种针对患者优化的隐私保护数据包转发方案POP,以保护PHI的机密性和患者的身份/位置隐私,减少中级患者受到威胁时的隐私暴露并实现了移动医疗系统中所需的患者优​​化属性。最后但并非最不重要的一点是,安全性分析表明,我们提出的GTSIM和POP都可以抵抗各种复杂的攻击,并且广泛的仿真证明了实用性和效率高,消息传递率高,实用率低且平均延迟低。 (C)2019 Elsevier B.V.保留所有权利。

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