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Honest cooperation among individuals in a network can be achieved indifferent ways. In online networks with some kind of central authority, such asEbay, Airbnb, etc. honesty is achieved through a reputation system, which ismaintained and secured by the central authority. These systems usually rely onreview mechanisms, through which agents can evaluate the trustworthiness oftheir interaction partners. These reviews are stored centrally and aretamper-proof. In decentralized peer-to-peer networks, enforcing cooperationturns out to be more difficult. One way of approaching this problem is byobserving cooperative biological communities in nature. One finds thatcooperation among biological organisms is achieved through a mechanism calledindirect reciprocity. This mechanism for cooperation relies on some sharednotion of trust. In this work we aim to facilitate communal cooperation in apeer-to-peer file sharing network called Tribler, by introducing a mechanismfor evaluating the trustworthiness of agents. We determine a trust ranking ofall nodes in the network based on the Monte Carlo algorithm estimating thevalues of Google's personalized PageRank vector. We go on to evaluate thealgorithm's resistance to Sybil attacks, whereby our aim is for sybils to beassigned low trust scores.

A Random Walk based Trust Ranking in Distributed Systems