Green: Towards a Pollution-Free Peer-to-Peer Content Sharing Service

TL;DR

Green is a novel P2P content sharing system that effectively combats content pollution attacks by leveraging content-based information, social reputation, and real-time verification, ensuring authentic content delivery with minimal overhead.

Contribution

The paper introduces Green, a pollution-free P2P system that combines reputation modeling, social information, and probabilistic verification to prevent content pollution without significant performance costs.

Findings

Green successfully detects and filters polluted content in large-scale networks.

The system maintains high content authenticity with low overhead.

Green outperforms existing methods in preventing content pollution attacks.

Abstract

Peer-to-Peer (P2P) content sharing systems are susceptible to the content pollution attack, in which attackers aggressively inject polluted contents into the systems to reduce the availability of authentic contents, thus decreasing the confidence of participating users. In this paper, we design a pollution-free P2P content sharing system, Green, by exploiting the inherent content-based information and the social-based reputation. In Green, a content provider (i.e., creator or sharer) publishes the information of his shared contents to a group of content maintainers self-organized in a security overlay for providing the mechanisms of redundancy and reliability, so that a content requestor can obtain and filter the information of his requested content from the associated maintainers. We employ a reputation model to help the requestor better identify the polluted contents, and then utilize the social (friend-related) information to enhance the effectiveness and efficiency of our reputation model. Now, the requestor could easily select an authentic content version for downloading. While downloading, each requestor performs a realtime integrity verification and takes prompt protection to handle the content pollution. To further improve the system performance, we devise a scalable probabilistic verification scheme. Green is broadly applicable for both structured and unstructured overlay applications, and moreover, it is able to defeat various kinds of content pollution attacks without incurring significant overhead on the participating users. The evaluation in massive-scale networks validates the success of Green against the content pollution.