H. Vickey Zhao of University of Alberta explores how users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems. In video-sharing social networks, users have access to extra resources from their peers, and they also contribute their own resources to help others. Each user wants to maximize his/her own payoff, and they negotiate with each other to achieve fairness and address this conflict. However, some selfish users may cheat to their peers and manipulate the system to maximize their own payoffs, and cheat prevention is a critical requirement in many social networks to stimulate user cooperation. It is of ample importance to design monitoring mechanisms to detect and identify misbehaving users, and to design cheat-proof cooperation stimulation strategies. Using video fingerprinting as an example, this paper analyzes the complex dynamics among attackers during multiuser collusion, and explores possible monitoring mechanisms to detect and identify misbehaving colluders in multiuser collusion.
An exploration of how users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems.