Adding trust to P2P distribution of paid content

6Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.
Get full text

Abstract

While peer-to-peer (P2P) file-sharing is a powerful and cost-effective content distribution model, most paid-for digital-content providers (CPs) use direct download to deliver their content. CPs are hesitant to rely on a P2P distribution model because it introduces a number of security concerns including content pollution by malicious peers, and lack of enforcement of authorized downloads. Furthermore, because users communicate directly with one another, the users can easily form illegal file-sharing clusters to exchange copyrighted content. Such exchange could hurt the content providers' profits. We present a P2P system TP2P, where we introduce a notion of trusted auditors (TAs). TAs are P2P peers that police the system by covertly monitoring and taking measures against misbehaving peers. This policing allows TP2P to enable a stronger security model making P2P a viable alternative for the distribution of paid digital content. Through analysis and simulation, we show the effectiveness of even a small number of TAs at policing the system. In a system with as many as 60% of misbehaving users, even a small number of TAs can detect 99% of illegal cluster formation. We develop a simple economic model to show that even with such a large presence of malicious nodes, TP2P can improve CP's profits (which could translate to user savings) by 62% to 122%, even while assuming conservative estimates of content and bandwidth costs. We implemented TP2P as a layer on top of BitTorrent and demonstrated experimentally using PlanetLab that our system provides trusted P2P file sharing with negligible performance overhead. © 2009 Springer Berlin Heidelberg.

Cite

CITATION STYLE

APA

Sherman, A., Stavrou, A., Nieh, J., Keromytis, A. D., & Stein, C. (2009). Adding trust to P2P distribution of paid content. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5735 LNCS, pp. 459–474). https://doi.org/10.1007/978-3-642-04474-8_36

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free