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-domain-focused 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.

众包和众计算是一种可能越来越流行的趋势，但仍然存在许多需要解决的研究和运营挑战。以人为中心的计算抽象（称为情境）可以用来应对这些困难。在本文中，我们关注这样一个挑战，即如何高效、有效和准确地将有关在线社交网络中的安全和隐私的人群评估任务分配给最合适的用户。具体来说，我们在这里提出了一种新的任务分配方法来促进人群评估，从而提高社交网络平台的安全性和可信度，以及基于SocialSitu的任务分配算法，这是一种以社交领域为中心的情境分析。我们在现实世界的社交网络 Shareteches 上进行的人群评估实验结果表明，所提出的方法和算法的精确度和召回率比随机算法高 0.491 和 0.538，比用户主题感知高 0.336 和 0.366算法的，分别。此外，这些结果进一步表明我们的实验评估增强了在线社交网络的安全性和隐私性。