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Location Privacy Preservation Scheme Based Incentive for Crowd-Sensing Networks

机译:基于位置隐私保护方案的人群感知网络激励

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摘要

Mobile crowd sensing (MCS) takes advantage of pervasive smart phones equipped with multi-sensors to collect sensing data from social events and phenomena for conducting complex sensing tasks. To achieve high quality of sensing service, on one hand, it is inevitable for mobile user to share the privacy information (e.g., identification, location, interest, etc.) with other mobile users. On the other hand, the willingness for mobile user to complete sensing task is low without incentive. Therefore, the privacy preservation and incentive scheme become critical issues that need to be considered. In this paper, a privacy preservation scheme based on differential privacy method is firstly proposed to protect the location privacy of mobile users. Secondly, the selected mobile users act as the task responders to complete sensing tasks. After that, the interaction behavior between mobile requester and mobile users is modelled as the reverse auction game, where the optimal bidding strategy is obtained by analyzing the optimization problem to stimulate mobile users to join in crowd sensing. Finally, simulation results have been conducted to validate the efficiency and effectiveness of the proposed scheme.
机译:移动人群感知(MCS)利用配备了多传感器的普及型智能手机来收集来自社交事件和现象的感知数据,以执行复杂的感知任务。为了实现高质量的传感服务,一方面,移动用户不可避免地要与其他移动用户共享隐私信息(例如,标识,位置,兴趣等)。另一方面,没有激励,移动用户完成感知任务的意愿较低。因此,隐私保护和激励方案成为需要考虑的关键问题。本文首先提出了一种基于差分隐私法的隐私保护方案,以保护移动用户的位置隐私。其次,选定的移动用户充当任务响应者来完成传感任务。此后,将移动请求者与移动用户之间的交互行为建模为反向拍卖游戏,通过分析优化问题来激发移动用户加入人群感知,从而获得最优出价策略。最后,通过仿真结果验证了所提方案的有效性和有效性。

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