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Privacy Protection for Context-A ware Services: A Two-Layer Three-Party Game Model

机译:上下文软件服务的隐私保护:两层三方博弈模型

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In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. However, in order to benefit from these services, users are enduring the risk of leaking private information. Game theory is a powerful method that is utilized to balance such tradeoff problems. The drawback is that most schemes consider the tradeoff problem from the aspect of the users, while the platform is the party that dominates the interaction in reality. There is also an oversight to formulate the interaction occurring between multiple users, as well as the mutual influence between any two parties involved, including the user, platform and adversary. In this paper, we propose a platform-centric two-layer three-party game model to protect the users' privacy and provide quality of service. One layer focuses on the interactions among the multiple asymmetric users and the second layer considers the influence between any two of the three parties (user, platform, and adversary). We prove that the Nash Equilibrium exists in the proposed game and find the optimal strategy for the platform to provide quality service, while protecting private data, along with interactions with the adversary. Using real datasets, we present simulations to validate our theoretical analysis.
机译:在上下文感知服务时代,基于从大量用户收集的数据,用户正在享受卓越的服务。但是,为了从这些服务中受益,用户承受着泄露私人信息的风险。博弈论是一种强大的方法,可用于平衡此类权衡问题。缺点是大多数方案从用户的角度考虑权衡问题,而平台则是在现实中主导交互的一方。还有一个监督机制来制定多个用户之间发生的交互以及所涉及的任何两个方(包括用户,平台和对手)之间的相互影响。在本文中,我们提出了一个以平台为中心的两层三方游戏模型,以保护用户的隐私并提供服务质量。一层侧重于多个不对称用户之间的交互,而第二层则考虑了三方(用户,平台和对手)中任何两个方之间的影响。我们证明了拟议的游戏中存在纳什均衡,并找到了平台的最佳策略,可在提供优质服务的同时保护私人数据以及与对手的互动。使用真实的数据集,我们提供模拟以验证我们的理论分析。

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