Video streaming has become increasingly popular in the Internet. Frequently, video transmissions are based on peer-to-peer networks, in which peers running on end-user hosts transmit data among themselves. An important security vulnerability of this strategy is that content can be easily altered by malicious users. Thus, it becomes essential to diagnose and fight content pollution in these systems. In this work, the authors present a novel strategy that relies on comparison-based diagnosis to mitigate content pollution in live video streaming peer-to-peer networks. This strategy is fully distributed and effectively combats the dissemination of content pollution. In the strategy, peers independently identify and avoid polluters. The solution works on top of the scalable overlay network Fireflies. Experimental results are presented showing the effectiveness and the low overhead of the solution. In particular, the strategy was able to significantly reduce content pollution propagation in diverse network configurations.

中文翻译：

---

分布式缓解点对点视频流网络中的内容污染

视频流在Internet中变得越来越流行。通常，视频传输是基于对等网络的，其中在最终用户主机上运行的对等网络之间进行数据传输。此策略的一个重要安全漏洞是恶意用户可以轻松更改内容。因此，在这些系统中诊断和消除内容污染变得至关重要。在这项工作中，作者提出了一种新颖的策略，该策略依靠基于比较的诊断来减轻实时视频流对等网络中的内容污染。此策略已完全分发，并有效地防止了内容污染的传播。在该策略中，同伴独立地识别并避免污染者。该解决方案在可扩展的覆盖网络Fireflies之上运行。实验结果表明该解决方案的有效性和低开销。特别是，该策略能够显着减少各种网络配置中的内容污染传播。