![]() ![]() ![]() Along with such advancement, there come frequent user privacy disclosures that have attracted wide attention from academia and the industry. The rapid development of cloud computing and big data technologies has greatly promoted work productivity and life quality. ![]() ![]() Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. 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. 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). ![]()
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