Multi-Trust-Incentives

Multi-Trust-Incentives

The major problem of private history based incentive systems is their coverage. Resolving it requires leveraging other reputable peers’ history which leads directly to the EigenTrust mechanism. Multi-Trust-Incentives try to mix both mechanisms.

Design of Multi-Trust-Incentives

Mathematical View

One-Step-Matrix

align="right" The Evaluation of Trust between peers is measured in a Matrix M. This N * N matrix defines a one-step rank among peers. All values are measured as the normalized download volume that a peer i has received from a peer j during a period of time.

} Two-Step-Matrix A two-step-matrix $M^{2}$ describes the relation in 3 levels. Implementation Idea For a duration of t, a peer i computes his own matrix by normalizing all the downloads it received from a peer j. Periodically i will ask j for j's immediate friends so j computes its own matrix. This process is repeated iteratively until i can not get any more matrices from j. costs within the Maze Within the Maze Network a peer i has about 36 friends for one day in average. Gathering Information about one-level friends needs 32KB data space in total. Even with level two, it becomes about 1MB for information about peers. Furthermore a daily update does not produce any significant overhead. But moving to higher levels costs for peer information are progressively growing. In the end this Multi-Trust Incentive was developed just for level two because it already covers more than 60% of total traffic. Implementation in the Maze Performance Coverage Experiment Lag-Hugger Experiment Satellite Cluster Experiment Evaluation External Links