A crowdsourcing method for online social networks security assessment based on human-centric computing

Zhiyong Zhang,Brij B Gupta,Xiaoxue Wang,Kim-Kwang Raymond Choo,Junchang Jing

doi:10.1186/s13673-020-00230-0

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-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.

Highlights

Mobile devices are often used by online social network (OSN) users to access, share, and exchange information, whenever and wherever possible [1,2,3]
(2) we propose a task assignment algorithm to determine user task suitability based on SocialSitu, in order to achieve efficient and accurate assignment of crowd tasks about security and privacy
In “Experimental setup and analysis” section, we describe the crowd assessment experiments and discuss the findings which show that the proposed approach facilitates efficient task assignment and improves the task precision and recall

Summary

Introduction

Mobile devices are often used by online social network (OSN) users to access, share, and exchange information, whenever and wherever possible [1,2,3]. Most current assignment methods are unable to find suitable crowds for certain tasks; leading to low completeness, precision, and recall To solve these problems, this study defines the decision factors that affect task assignment and suitability of user tasks in online social networks. The current research completely considers users’ functional experience and historical information, which are used as bases to design a task assign method of crowd assessment for the security of online social networks. Hierarchical assign structure Due to online social network task assignment crowd selection existing many optional schemes, among the degree of completion, service response time, the degree of correlation, operation behaviors, and coincidence for each schemes have strong fuzziness.

Data identification

Findings

Conclusion