Along with the convenience brought hy the open social media platforms for the users,there are also many security and trustworthiness problems like malicious websites,information cheating and lack of trust.Security and trustworthiness of social platforms,as the foundation of social interactions,play an important role in information sharing and communication.The traditional evaluations of security and trust are only focused on trust relationship among users and security implementation,however the evaluation and measurement for social platforms have not yet been well done.Therefore a novel method to evaluate the security and trust of the online social network platforms based on signaling theory in information management science is proposed.Firstly,we classified the signals of security and trust of the generic OSNs platform itself,and formalized static attributes and dynamic behaviors features with the OWL and the temporal logic.Then,a FAHP holistic evaluation was made to confirm signals' indicators weight,and a comprehensive security and trust evaluation computation model was presented by adopting the idea of crowd computing.Finally,an evaluation experiment was carried out on a real multimedia social network platform called CyVOD.net.The experimental results denote that the proposed approach can accurately gain the assessment values of each security and trust element of social platforms,and give effetive guidances for functional evolutions and edition updates for social media platforms.%开放的社交媒体平台给用户带来使用便捷的同时,也存在不少恶意站点、信息欺骗和信任缺失等安全性和可信性问题.社交平台的安全性和可信性作为社会交互的基础,在信息共享与交流中至关重要;传统的安全和信任评估仅关注于用户间的信任关系以及安全实现,而针对社交媒体平台的评估和度量方法还不健全;因此提出了一种基于信息管理信号理论的在线社交网络平台安全和信任度量方法;首先,对平台安全性和可信性信号进行分类,并采用OWL语言和时态逻辑形式化描述了平台静态属性和动态行为特征;其次,使用FAHP确定此类信号的指标权重并进行系统的评价,并结合群体计算思想提出了一个平台安全和信任的综合评估计算模型;最后,在一个现实的多媒体社交网络平台(CyVOD.net)上进行了评估实验;实验结果显示,该方法能够准确地获得社交平台的各安全和信任要素的评估值,并有效地指导社交媒体网络平台的功能进化和版本更新.
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