A crowdsourcing method for online social networks security assessment based on human-centric computing
Human-Centric Computing and Information Sciences
Introduction Mobile devices are often used by online social network (OSN) users to access, share, and exchange information, whenever and wherever possible    . One relatively recent trend is crowdsourcing for information, share resources, and/or to get certain tasks done, as evidenced by the increasing number and variety of crowdsourcing applications, such as Amazon Mechanical Turks, chats (e.g., Firechat), knowledge sharing (e.g., Fig. 1 ), ridesharing (e.g., Uber) and accommodation
... aring (e.g., Airbnb). Crowdsourcing  often refers to a solution pattern which outsources the tasks that were previously performed by full-time employees to non-specific solution providers via public online platforms. Such crowdsourcing can be voluntary (unpaid) or paid. Crowd computing  is a computing model, which aims to integrate numerous users who may know each other (i.e., the crowd) and computing resources (i.e., Abstract Crowdsourcing and crowd computing are a trend that is likely to be increasingly popular, and there remain a number of research and operational challenges that need to be addressed. The human-centric computational abstraction called situation may be used to cope with these difficulties. In this paper, we focus on one such challenge, which is how to assign crowd assessment tasks about security and privacy in online social networks to the most appropriate users efficiently, effectively and accurately. Specifically, here we propose a novel task assignment method to facilitate crowd assessment, which improves the security and trustworthiness of social networking platforms, as well as a task assignment algorithm based on SocialSitu, which is a social-domainfocused situational analytics. Findings from our crowd assessment experiments on a real world social network Shareteches show that the precision and recall of the proposed method and algorithm are 0.491 and 0.538 higher than those of a random algorithm's, as well as 0.336 and 0.366 higher than users' theme-aware algorithm's, respectively. Moreover, these results further suggest that our experimental evaluation enhance the security and privacy of online social networks.