期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2018
卷号：60
期号：2
DOI：10.14445/22312803/IJCTT-V60P111
出版社：Seventh Sense Research Group
摘要：The past few years have been witnessing the theatrical popularity of largescale social networks, where malicious nodes contact is one of the essential troubles. Most existing works focus on actively detecting malicious nodes by verifying signal relationship or actions consistency. It may not work well in largescale social networks since the number of users is enormously large and the variation between normal users and malevolent users is unremarkable. In this paper, we recommend a novel approach that leverages the ability of users to present the discovery task. We intend motivation mechanism to persuade the contribution of users under two scenarios Full Information and Partial Information. In full information scenario, we design a specific encouragement scheme for users according to their preferences, which can provide the desirable detection result and minimize overall cost. In partial information scenario, assuming that we only have statistical information about users, we first transform the incentive mechanism design to an optimization problem, and then design the optimal incentive scheme under different system parameters by solving the optimization problem. We perform extensive simulations to validate the analysis and demonstrate the impact of system factors on the overall cost.
关键词：Crowd sourcing; Social Networks; Malevolent Users Revealing; Big Data.
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