Privacy Protection Based On Game Analysis In Mobile Crowdsensing

Mobile crowdsensing,as a new Internet of Things sensing model that combines mobile sensing and crowdsourcing,bridges the gap between mobile terminal hardware infrastructure and intelligent ubiquitous depth sensing and computing.However,how to effectively motivate users to continuously participate in the sensing tasks while protecting their identity privacy,how to effectively measure the privacy of sensing data,and how to ensure the uploading of sensing data and transaction privacy have not been effectively resolved.In view of the above issues,based on game analysis,this dissertation focuses on the theory and key of sensing data lifecycle privacy protection from the aspects of privacy incentives,privacy measurement and privacy protection.Research works are as follows:1.In order to solve the issue that the sensing users in the perception stage are worried about privacy leakage,which leads to low motivation of task participation and low guaranteed completion rate,a hybrid incentive mechanism based on user alliance matching is proposed,which uses the incentive mechanism of mixed task revenue,reputation score and service return to motivate more high-quality sensing users to participate in mobile crowdsensing tasks.In addition,before uploading the sensing data,the sensing users can choose to use Bloom filter and the inner product of binary obfuscation vectors to estimate the similarity to form a sensing user alliance,and then the sensing dataset to protect the privacy of users while ensuring the quality of the sensing data.Finally,the simulation results show the effectiveness of the proposed mechanism.2.In mobile crowdsensing,existing privacy protection schemes lack the dynamics of user privacy protection,thereby resulting in excessive or insufficient protection of privacy information and low accuracy of sensing data.To tackle these issues,a personalized privacy protection framework based on game theory and sensing users' historical spatio-temporal data is proposed.Through game analysis,the user can guide the user to select the optimal strategy to maximize the user's utility and provide strong privacy protection for the user.The performance of the proposed scheme is analyzed by simulation experiments of real trajectory data.3.Aiming at the issues of privacy leakage and prisoner's dilemma caused bymalicious transactions between cloud service providers and sensing platforms in the data transaction stage of mobile crowdsensing,a secure multi-party auction scheme for mobile crowdsensing data transaction is studied by using secret sharing and group signature algorithm.Based on the goal of maximizing revenue,even in the face of collusion between service providers,participants can participate in data auction and transaction data value,while achieving the purpose of protecting the privacy and security of participants,solving prisoners' dilemma and maximizing the interests of participants.