EigenRep: Reputation Management in P2P Networks

TLDR

Peer‑to‑peer file‑sharing networks are widely used for distributing information, yet their anonymous, open nature facilitates the spread of self‑replicating inauthentic files. The authors propose an algorithm that assigns each peer a unique global reputation value derived from its upload history to reduce the download of inauthentic files. They implement a distributed, secure computation of these reputation values using power iteration, and peers select download partners based on the resulting reputations, thereby isolating malicious nodes. Simulations demonstrate that this reputation system markedly lowers the prevalence of inauthentic files, even when malicious peers collaborate to subvert it.

Abstract

Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience with P2P networks such as Gnutella shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files. We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global reputation value, based on the peer’s history of uploads. We present a distributed and secure method to compute global reputation values, based on Power iteration. By having peers use these global reputation values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network. In simulations, this reputation system has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system.