Combining search and trust models in unstructured peer-to-peer networks

Effectiveness of Peer-to-Peer (P2P) systems highly depends on efficiency and scalability of their search algorithms. Moreover, managing trust is a key issue for wide acceptance of P2P computing. Surprisingly, the majority of the available trust systems ignore the underlying search algorithm and assume it is preexisting. We claim that combining search and trust systems yields significant performance gains in terms of network traffic and query success rate. In this paper, we propose a robust and efficient trust based search framework for unstructured P2P networks. Our framework maintains limited size routing indexes combining search and trust data to guide queries to most reputable nodes. By dynamically selecting reputable nodes as score managers, our scheme tracks the reputation of participating peers. In an alternative approach, we aggregate partial reputation values obtained from reverse query paths to introduce a low overhead method for estimating reputation scores of peers. Through P2P network simulation experiments, we find significant performance gains in using our framework.