Study On Trust Mechanisms Of Social Networks

Trust is a common and important concept in human life because of the human society.There are many things related to trust in our daily.At present,the trust problem of social networks has become the key problem in the field of information security.The trust technologies have become the extension of traditional network security technologies.The current social networks are laid in a dynamic and open environment,which makes social networks face the significant challenges: the social networks with more and more complex structures,become more and more fragile;the problems of security,reliability and usability loom large,so that make social networks cannot work in the way that people desire and trust.Therefore,according to the openness of social networks,the trust mechanism of social networks under complex environment is researched in this paper.The trust mechanism of social networks involves the problems of trust measurement,trust recommendation,trust evolution and incentive mechanism.This paper researches the trust mechanism under complex environment according to the shortages of the traditional methods.The main research works of this paper are shown as follows.(1)In order to measure one node's trust degree in social networks more accurately,this paper establishes a trust measurement model based on game theory.The trust degree of one node is measured through combining global trust with local trust.In order to avoid the generation of malicious collectives,this paper researches the community detection method and discovery of cooperative cheating based on evaluation similarity of social networks.(2)In order to improve the accuracy of trust measurement in social networks,this paper studies the multidimensional dynamic trust measurement model of social networks based on the characteristics of forgetting curve in psychology and information entropy theory.The direct trust,indirect trust,similarity and incentive function are considered into the establishment of the dynamic trust measurement model for social networks.(3)In order to improve the accuracy of recommendation model in social networks,the transition probability of products is derived based on probabilistic model through considering non-personality and personality methods.Combining recommendation attributes with inherent similarity among products together to determine the transition probability of products comprehensively.Then this paper computes trust degree of products based on their reputations and purchase frequencies.Inaddition,this paper solves the problem of users' cold start based on users' latent factors.In the end,this paper establishes trust recommendation model of social networks according to the above factors.(4)According to the typical shortages of the traditional evolution model,consideringtime-sensitive of history data and aggregation degree of entity in complex environment,the entities' utility values are computed based on fuzzy theory.A multi-strategy trust evolution model with white noise for social networks based on Wright-Fisher and evolutionary game theory is established.(5)In order to solve the free-riding problem,an incentive mechanism based on the evolutionary game theory is proposed to inspire entities to select strategies with high trust degree.Therefore,the whole trust degree of a social network will be improved.According to the problems of trust mechanisms for social networks,this paper establishes a trust mechanism of social networks under complex environment.First of all,in order to measure a node's trust degree in social networks more accurately,a trust measurement model based on game theory for social networks is established.Most of the traditional trust measurement models have the following shortages: lack of adaptability;single dimension;fail to consider the time-sensitive of history data;cannot effectively deal with strategic dynamic changing behavior of entities in social networks.Thus this paper proposes a multidimensional dynamic trust measurement model based on forgetting curve of psychology and information entropy theory.Combining non-personality recommendation with personality recommendation,a trust-based probabilistic recommendation model for social networks is proposed in order to improve the recommendation accuracy.In addition,the cold start problem of a new user is solved based on the latent factors of products.In order to predict trust evolution trend of social networks more accurately,combining with the complexity of social networks,a Wright-Fisher multi-strategy trust evolution model with white noise for social networks is established.Finally,in order to solve free-riding problem,and improve the trust degree of a network,this paper builds an incentive mechanism based on evolutionary game to inspire entities to select the strategies with high trust degree.