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A Geo-indistinguishable Location Privacy Preservation Scheme for Location-Based Services in Vehicular Networks

机译:车载网络中基于位置的服务的地理上无法区分的位置隐私保护方案

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In vehicular networks, the location-based services (LBSs) are very popular and essential for most vehicular applications. However, large number of location information sharing may raise location privacy leakage of in-vehicle users. Since the existing privacy protection mechanisms ignore the trajectory information, so that the location privacy of in-vehicle users and the trade-off between privacy and quality of service (QoS) cannot be effectively solved. In order to provide satisfactory privacy and QoS for in-vehicle users, in this paper, we propose an improved geo-indistinguishable location privacy protection scheme (GLPPS). Specifically, we first select an area of service retrieval (ASR) instead of user's real location to send to LBS, which can protect location privacy while avoiding the leakage of trajectory. Secondly, we establish an income model based on Stackelberg between in-vehicle users and attackers, and design an IM-ASR algorithm based on the Iterative method and Maximin theorem, to achieve optimal trade-off between privacy and QoS. Finally, we prove that GLPPS satisfies Q-differential privacy. Moreover, the simulation results demonstrate that GLPPS can reduce loss of QoS while improving location privacy compared to other methods.
机译:在车辆网络中,基于位置的服务(LBS)非常流行,并且对于大多数车辆应用而言都是必不可少的。但是,大量的位置信息共享可能会增加车载用户的位置隐私泄漏。由于现有的隐私保护机制忽略了轨迹信息,因此无法有效解决车载用户的位置隐私以及隐私与服务质量(QoS)之间的权衡问题。为了为车载用户提供令人满意的隐私和QoS,我们提出了一种改进的地理上无法区分的位置隐私保护方案(GLPPS)。具体来说,我们首先选择一个服务检索区域(ASR)而不是用户的真实位置发送到LBS,这可以在避免轨迹泄漏的同时保护位置隐私。其次,我们建立了基于Stackelberg的车载用户与攻击者之间的收入模型,并设计了基于迭代方法和Maximin定理的IM-ASR算法,以实现隐私与QoS的最佳权衡。最后,我们证明GLPPS满足Q差分隐私。此外,仿真结果表明,与其他方法相比,GLPPS可以减少QoS的损失,同时改善位置隐私。

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