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Title: Framework for preserving security and privacy in peer-to-peer content distribution systems
Author: Qureshi, Amna  
Megías Jiménez, David  
Rifà Pous, Helena  
Keywords: Privacy
Collusion-resistant fingerprinting
Peer-to-Peer networks
Issue Date: 15-Feb-2015
Publisher: Elsevier
Published in: Expert Systems with Applications:Volume 42, Issue 3,15 February 2015,Pages 1391-1408,ISSN 0957-4174
Abstract: The use of Peer-to-Peer (P2P) networks for multimedia distribution has spread out globally in recent years. The mass popularity is primarily driven by efficient distribution of content, also giving rise to piracy. An end user (buyer) of a P2P content distribution system does not want to reveal his/her identity during a transaction with a content owner (merchant), whereas the merchant does not want the buyer to further distribute the content illegally. Therefore, there is a strong need for a content distribution mechanism over P2P networks that do not pose security and privacy threats to the copyright holders and end users, respectively. The existent systems for copyright and privacy protection employ cryptographic mechanisms at a cost of high computational burden which makes these systems impractical to distribute large sized files, such as music albums or movies. In this paper, we propose and analyse a P2P content distribution system which allows efficient distribution of large-sized content while preserving the security and privacy of merchants and buyers, respectively. Our proposed framework is able to resolve the problems of piracy tracing, buyer frameproofness, collusion resistance, dispute resolution and buyer¿s anonymity. We have carried out simulations to evaluate the performance of our framework in terms of imperceptibility, robustness, throughput and content delivery costs. The experimental results confirm that the proposed framework provides an efficient solution to copyright infringement issues over P2P networks, reducing the multimedia file sizes as much as five times on average, while protecting the end users' privacy and anonymity.
Language: English
ISSN: 0957-4174MIAR
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