It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.

中文翻译：

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通过社交物联网中的贝叶斯游戏保护隐私的个性化服务框架

在服务质量（QoS）和用户的隐私保护之间实现令人满意的平衡，以及在社交物联网（IoT）中衡量隐私披露，都面临着巨大的挑战。我们提出了一种基于静态贝叶斯游戏的隐私保护个性化服务框架（波斯语），可以根据社交IoT中用户的个人安全要求提供隐私保护。我们的方法量化了用户的个人隐私偏好，并使用模糊不确定性推理对用户进行分类。这些分类结果有助于可信赖的云服务提供商（CSP）向用户提供相应级别的服务。此外，CSP做出战略选择，其目标是通过与潜在对手进行决策博弈来最大化声誉。我们的方法根据游戏混合策略均衡的概率，使用Shannon信息熵来衡量隐私披露的程度。实验结果表明，尽管存在对手，波斯语仍可保证QoS并有效保护用户隐私。