Robust incentives via multi-level Tit-for-Tat

Much work has been done to address the need for incentive models in real deployed peer-to-peer networks. In this paper, we discuss problems found with the incentive model in a large, deployed peer-to-peer network, Maze. We evaluate several alternatives, and propose an incentive system that generates preferences for wellbehaved nodes while correctly punishing colluders. We discuss our proposal as a hybrid between Tit-for-Tat and EigenTrust, and show its effectiveness through simulation of real traces of the Maze system.

• Type: Journal
• Year: 2008
• Where: CONCURRENCY
• Authors: Qiao Lian, Yu Peng, Mao Yang, Zheng Zhang, Yafei Dai, Xiaoming Li
• Keywords: CONCURRENCY 2008, Incentive Model, Maze, Peer-to-peer Network