Coping with bad-mouthing in peer-to-peer file sharing networks

In the recent years, the P2P file sharing systems have adopted rating systems in the hope to stop the propagation of bad files. In a rating system, users rate files after downloading and a file with positive feedback is considered a good file. However, a dishonest rater can undermine the rating system by giving positive rating to bad files and negative rating to good files. In this paper, we design two filters based on probabilistic models such that the good files with negative feedback are not completely kept out of the system. The first filter is based on the binomial distribution of the ratings of a file, and the second filter considers the confidence of the downloading peer and the difference of positive and negative ratings of a file to calculate the probability to take a risk to download the file or reject it. Our filters only need the ratings of a file and this makes them suitable for popular torrent sharing websites that rank the files using a binary rating system without any information about raters. In addition, we can implement them entirely on the client side without any modification to the content sharing sites.