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Robust Incentives via Multi-level Tit-for-tat

Qiao Lian, Peng Yu, Mao Yang, Zheng Zhang, Yafei Dai, and Xiaoming Li


Much work has been done to address the need for incentive models in real deployed peertopeer networks. In this paper, we discuss problems found with the incentive model in a large, deployed peertopeer 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 TitforTat and EigenTrust, and show its effectiveness through simulation of real traces of theMaze system.


Publication typeInproceedings
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