LBSs have been prospering over recent years. Although many LPPMs are proposed to solve specific problems under certain settings, it is still hard to satisfy the different demands of users due to the diverse requirements for quality of service (QoS) and privacy preservation. In this article, we propose an adaptive scheme to achieve fine-grained QoS for privacy-aware users in LBSs. Specifically, we construct a Bayesian-based classification model to identify the QoS requirements of users under different circumstances. QoS and privacy degrees provided by LBSs are carefully defined based on the concept of rate-distortion theory, which plays an important role in bridging the LPPMs with users' requirements. When users request the location service from the built-in GPS module in smart devices, we first specify their circumstances and requirements, then reply with the modified locations. We also design an efficient algorithm to dynamically choose the appropriate privacy strategy according to the QoS requirements of users in different circumstances. Experimental results show that our scheme can identify QoS requirements accurately and protect location privacy adaptively.

中文翻译：

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为LBS中的隐私感知用户实现精细的QoS

近年来，LBS一直在蓬勃发展。尽管提出了许多LPPM来解决特定设置下的特定问题，但是由于对服务质量（QoS）和隐私保护的要求不同，仍然难以满足用户的不同需求。在本文中，我们提出了一种自适应方案，可为LBS中的具有隐私意识的用户实现细粒度的QoS。具体来说，我们构建基于贝叶斯的分类模型，以识别不同情况下用户的QoS要求。LBS提供的QoS和隐私度是根据速率失真理论的概念精心定义的，它在将LPPM与用户需求联系在一起方面发挥着重要作用。当用户从智能设备中的内置GPS模块请求定位服务时，我们首先会指定他们的情况和要求，然后回复修改后的位置。我们还设计了一种有效的算法，可以根据不同情况下用户的QoS要求动态选择合适的隐私策略。实验结果表明，该方案能够准确识别QoS要求，并自适应地保护位置隐私。